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type: PagedCollection
items per page: 100
last: https://data.amerigeoss.org/catalog.ttl
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total items: 645296

AmeriGEOSS Community Platform DataHub. (BETA)
type: Catalog
description: AmeriGEOSS Community Platform DataHub. (BETA)
language: en
modified: 2026-02-19T22:31:03.326118

Afghanistan - Existing and Planned Power Plants
type: Dataset
description: Operating power plants in Afghanistan as in 2016. Digitization based on Afghan Energy Information Center (AEIC) maps.
issued: 2025-09-18T20:30:17.721684
modified: 2026-02-19T22:30:18.788032
contact point: Organization
  fn: Alexandros Korkovelos
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://aeic.af/map/1

Afghanistan Power Plants
type: Distribution
format: ZIP
issued: 2026-02-09T12:00:17.491071
modified: 2026-02-19T22:30:18.824055
access URL: https://energydata.info/dataset/04730d82-2bef-41bc-b304-f8c3f7127dbe/resource/653c1d95-3292-4388-a778-caacc012b216/download/afghanistanpowerplants2015.zip

Afghanistan Power Plants (Planned)
type: Distribution
format: ZIP
issued: 2026-02-09T12:00:58.814614
modified: 2026-02-19T22:30:18.824938
access URL: https://energydata.info/dataset/04730d82-2bef-41bc-b304-f8c3f7127dbe/resource/6598434d-8860-4223-9641-5952f76189f0/download/futurepowerplants.zip

Afghanistan Power Plants
type: Distribution
format: GeoJSON
issued: 2026-02-09T11:59:54.740628
modified: 2026-02-19T22:30:18.823577
access URL: https://energydata.info/dataset/04730d82-2bef-41bc-b304-f8c3f7127dbe/resource/92ab5cad-7981-4458-9164-06da500a3086/download/afgexistingpowerplantswgs84.geojson

Afghanistan Power Plants (Planned)
type: Distribution
format: GeoJSON
issued: 2026-02-09T12:00:41.063493
modified: 2026-02-19T22:30:18.824481
access URL: https://energydata.info/dataset/04730d82-2bef-41bc-b304-f8c3f7127dbe/resource/931c8226-3f5d-46d5-bc05-9e7caa4d12a2/download/afgfuturepowerplantswgs84.geojson

META tool: Inputs Generation Technology, Projected Costs
type: Dataset
description: This dataset contains the input parameters for Model for Electricity Technology Assessment (META) tool for US, Romania, Jamaica and India (base year 2010). Furthermore, the META projected costs for 2015 and 2020 are presented.
issued: 2025-09-18T20:30:20.909907
modified: 2026-02-19T22:30:20.923152
contact point: Organization
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
keyword: assessment
keyword: meta
keyword: projected costs
landing page: http://www.worldbank.org/

META_Inputs_GenerationTechnology_ProjectedCosts
type: Distribution
format: CSV
issued: 2026-02-09T12:01:42.657672
modified: 2026-02-19T22:30:20.926188
access URL: https://energydata.info/dataset/07030b1e-e0d3-4ee5-bd7f-200c5088e975/resource/9db0a619-c339-4a08-b96c-b46e60653c7e/download/metainputsgenerationtechnologyprojectedcosts.csv

Afghanistan - Annual Average Wind Speed Data
type: Dataset
description: Wind speed raster file over the territory of Afghanistan. Aggregated annual average values. Spatial resolution 5km.
issued: 2025-09-18T20:30:27.519891
modified: 2026-02-19T22:30:22.026230
contact point: Organization
  fn: Alexandros Korkovelos
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Afghanistan Wind Speed
type: Distribution
format: TIFF
issued: 2026-02-09T11:40:28.035820
modified: 2026-02-19T22:30:22.029485

India - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed, wind rose and wind power density potential in India. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
issued: 2025-09-18T20:30:33.587513
modified: 2026-02-19T22:30:23.162936
contact point: Organization
  fn: Albertine Potter van Loon
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://globalwindatlas.info

India - Wind Speed and Wind Power Potential Maps - GIS
type: Distribution
format: HTML
issued: 2026-02-09T13:39:17.146555
modified: 2026-02-19T22:30:23.166213
access URL: https://globalwindatlas.info/area/India

India - Wind Speed and Wind Power Potential Maps - Posters
type: Distribution
format: HTML
issued: 2026-02-09T13:39:49.705123
modified: 2026-02-19T22:30:23.166672
access URL: https://globalwindatlas.info/downloads/India

Senegal - Solar Radiation Measurement Data
type: Dataset
description: Ground measurement data from 3 solar meteorological stations in Senegal. Data contains 1 minute average values for solar radiation, air temperature, relative humidity, barometric pressure, precipitation, wind speed at 3m, wind speed calculated for 10m, wind direction and cleaning events. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/
issued: 2025-09-18T20:30:49.657104
modified: 2026-02-19T22:30:25.378993
contact point: Organization
  fn: GeoSUN Africa
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://globalsolaratlas.info/

Solar-Measurements_Senegal-Fatick_IFC_QC
type: Distribution
format: CSV
issued: 2026-02-09T12:03:16.322029
modified: 2026-02-19T22:30:25.382591
access URL: https://energydata.info/dataset/27754e48-beb3-486b-8dbb-b7b85ef74b1a/resource/2dd76196-af61-48cf-a4c1-599702ffc4a9/download/solar-measurementssenegal-fatickifcqc.csv

Solar-Measurements_Senegal-Touba_IFC_Header
type: Distribution
format: CSV
issued: 2026-02-09T12:04:14.567934
modified: 2026-02-19T22:30:25.383782
access URL: https://energydata.info/dataset/27754e48-beb3-486b-8dbb-b7b85ef74b1a/resource/35d94961-13c7-4e2c-a705-9e1771823c8d/download/solar-measurementssenegal-toubaifcheader.csv

Solar-Measurements_Senegal-Touba_IFC_QC
type: Distribution
format: CSV
issued: 2026-02-09T12:04:33.861519
modified: 2026-02-19T22:30:25.384155
access URL: https://energydata.info/dataset/27754e48-beb3-486b-8dbb-b7b85ef74b1a/resource/373f3da2-b203-4881-9d87-880f3e739351/download/solar-measurementssenegal-toubaifcqc.csv

Solar-Measurements_Senegal-Fatick_IFC_Header
type: Distribution
format: CSV
issued: 2026-02-09T12:02:58.658434
modified: 2026-02-19T22:30:25.382086
access URL: https://energydata.info/dataset/27754e48-beb3-486b-8dbb-b7b85ef74b1a/resource/43f3b3c2-9af3-4140-81d5-64e4a40898c8/download/solar-measurementssenegal-fatickifcheader.csv

Solar-Measurements_Senegal-Kahone_IFC_QC
type: Distribution
format: CSV
issued: 2026-02-09T12:03:53.745876
modified: 2026-02-19T22:30:25.383370
access URL: https://energydata.info/dataset/27754e48-beb3-486b-8dbb-b7b85ef74b1a/resource/47258b1a-fcd9-40c3-94c6-d4a2992f1466/download/solar-measurementssenegal-kahoneifcqc.csv

Solar-Measurements_Senegal-Kahone_IFC_Header
type: Distribution
format: CSV
issued: 2026-02-09T12:03:38.316422
modified: 2026-02-19T22:30:25.382991
access URL: https://energydata.info/dataset/27754e48-beb3-486b-8dbb-b7b85ef74b1a/resource/ecbc9dd2-c397-47aa-8bb8-f0c5154a3e5f/download/solar-measurementssenegal-kahoneifcheader.csv

Pakistan - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed, wind rose and wind power density potential in Pakistan. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
issued: 2025-09-18T20:30:58.273702
modified: 2026-02-19T22:30:26.675879
contact point: Organization
  fn: Albertine Potter van Loon
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://globalwindatlas.info

Pakistan - Wind Speed and Wind Power Potential Maps - GIS
type: Distribution
format: HTML
issued: 2026-02-09T13:40:55.125339
modified: 2026-02-19T22:30:26.678789
access URL: https://globalwindatlas.info/area/Pakistan

Pakistan - Wind Speed and Wind Power Potential Maps - Posters
type: Distribution
format: HTML
issued: 2026-02-09T13:41:15.108408
modified: 2026-02-19T22:30:26.679232
access URL: https://globalwindatlas.info/downloads/Pakistan

Honduras - Multi-Tier Framework (MTF) Survey
type: Dataset
description: The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (which includes raw data sets of dta format), excel spreadsheet, pdf, and docx.
issued: 2025-09-18T20:31:07.492803
modified: 2026-02-19T22:30:28.111632
contact point: Organization
  fn: Lucia Luzi, Abdul-Farouk Bemba Nabourema, Bryan Bonsuk Koo, Dana Rysankova, Elisa Portale
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

MTF_HONDURAS_Questionnaire_Household.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:48:58.832525
modified: 2026-02-19T22:30:28.116900
access URL: https://energydata.info/dataset/3ed8a465-33a1-4c59-8f26-b210f4691fbe/resource/0733d00b-1068-455b-a40c-62c9b0cfbab3/download/mtf_honduras_questionnaire_household.pdf

MTF Energy Access Country Diagnostics Report_Honduras
type: Distribution
format: PDF
issued: 2026-02-09T13:47:19.984989
modified: 2026-02-19T22:30:28.114723
access URL: https://energydata.info/dataset/3ed8a465-33a1-4c59-8f26-b210f4691fbe/resource/163d522f-0a77-4953-978f-de361a5d6b63/download/mtf-energy-access-country-diagnostics-report_honduras.pdf

Raw Data_Honduras.zip
type: Distribution
format: ZIP
issued: 2026-02-09T13:48:06.725955
modified: 2026-02-19T22:30:28.116029
access URL: https://energydata.info/dataset/3ed8a465-33a1-4c59-8f26-b210f4691fbe/resource/5cb66ecc-f0c4-4e04-b7a9-d93a0f06d6d9/download/raw-data_honduras.zip

Codebook_Honduras.xlsx
type: Distribution
format: XLSX
issued: 2026-02-09T13:47:54.213020
modified: 2026-02-19T22:30:28.115595
access URL: https://energydata.info/dataset/3ed8a465-33a1-4c59-8f26-b210f4691fbe/resource/6777371e-496d-416c-81f7-3cd9b2b39606/download/codebook_honduras.xlsx

Additional material for Questionnaire on solar devices.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:48:40.859976
modified: 2026-02-19T22:30:28.116459
access URL: https://energydata.info/dataset/3ed8a465-33a1-4c59-8f26-b210f4691fbe/resource/aa3eeb39-c2f5-4caf-97e7-73ddf9715b4c/download/note-for-questionnaire-on-solar-devices.pdf

MTF_Sampling_Strategy_HONDURAS.docx
type: Distribution
format: DOC
issued: 2026-02-09T13:47:39.637892
modified: 2026-02-19T22:30:28.115182
access URL: https://energydata.info/dataset/3ed8a465-33a1-4c59-8f26-b210f4691fbe/resource/cd7d6163-8ed1-4096-86b6-790ac6758492/download/mtf_sampling_strategy_honduras.docx

Nigeria - Jigawa State Settlement Dataset
type: Dataset
description: Data on Nigeria Settlement Location and Distribution downloaded from the The Bill & Melinda Gates Foundation Vaccination Tracking System (VTS). To learn more about the VTS project, please visit http://vts.eocng.org/Home/About?campaignId=0
issued: 2025-09-18T20:31:36.612099
modified: 2026-02-19T22:30:32.921995
contact point: Organization
  fn: Shaky Sherpa
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://vts.eocng.org/

Jigawa State Settlement
type: Distribution
format: CSV
issued: 2026-02-09T14:03:54.755372
modified: 2026-02-19T22:30:32.924787
access URL: https://energydata.info/dataset/6473cc90-07f4-4379-9afa-eb0cbcc9da7d/resource/6efafa59-b25d-4b4a-aab8-09b4ea9d3513/download/jigawastategatessettlement.csv

Jigawa State Settlement
type: Distribution
format: GeoJSON
issued: 2026-02-09T14:04:09.683468
modified: 2026-02-19T22:30:32.925270
access URL: https://energydata.info/dataset/6473cc90-07f4-4379-9afa-eb0cbcc9da7d/resource/8f59e3d3-62aa-47bc-a90d-0ba6d44ae3e4/download/jigawastategatessettlement.geojson

Kenya - Primary Substations
type: Dataset
description: The dataset contains primary substations in Kenya. The dataset was provided by Kenya Power and Lighting Company (KPLC).
issued: 2025-09-18T20:31:40.827237
modified: 2026-02-19T22:30:33.744630
contact point: Organization
  fn: Rhonda Jordan Antoine; Patrick Balla; Laurencia Njagi; Sudeshna Banerjee.
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://www.kplc.co.ke/

Primary Substations
type: Distribution
format: ZIP
issued: 2026-02-09T11:58:09.391927
modified: 2026-02-19T22:30:33.747590
access URL: https://energydata.info/dataset/6882b9c0-85e2-42a0-aa56-44128f549b42/resource/26b5b8fb-e64d-4498-883f-f66928008820/download/primary-substations.zip

Primary Substations
type: Distribution
format: JSON
issued: 2026-02-09T11:58:26.611689
modified: 2026-02-19T22:30:33.748020
access URL: https://energydata.info/dataset/6882b9c0-85e2-42a0-aa56-44128f549b42/resource/f3c7af48-8452-4408-bdca-bddae178378d/download/primary_substations.json

Nigeria - Kano State Settlement Dataset
type: Dataset
description: Data on Nigeria Settlement Location and Distribution downloaded from the The Bill & Melinda Gates Foundation Vaccination Tracking System (VTS). To learn more about the VTS project, please visit http://vts.eocng.org/Home/About?campaignId=0
issued: 2025-09-18T20:31:54.457292
modified: 2026-02-19T22:30:35.727470
contact point: Organization
  fn: Shaky Sherpa
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://vts.eocng.org/

Kano State Settlements
type: Distribution
format: CSV
issued: 2026-02-09T12:11:07.574061
modified: 2026-02-19T22:30:35.731243
access URL: https://energydata.info/dataset/7907600f-bc6c-4a39-a72a-628c19b79c91/resource/077bf8a0-7eca-47a6-b96a-1e78e7ce25a4/download/katsinastategatessettlement-1.csv

Kano State Settlement
type: Distribution
format: GeoJSON
issued: 2026-02-09T12:10:37.113530
modified: 2026-02-19T22:30:35.730806
access URL: https://energydata.info/dataset/7907600f-bc6c-4a39-a72a-628c19b79c91/resource/4ec32d35-d334-4115-9b35-58759ef6451b/download/kanostategatessettlement.geojson

Kenya - Healthcare Facilities
type: Dataset
description: Data on healthcare facility locations in Kenya. The dataset was provided by the Government of Kenya.
issued: 2025-09-18T20:31:58.318554
modified: 2026-02-19T22:30:36.474923
contact point: Organization
  fn: Rhonda Jordan Antoine; Patrick Balla; Laurencia Njagi; Sudeshna Banerjee.
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://www.opendata.go.ke/

Healthcare_Facilities.csv
type: Distribution
format: CSV
issued: 2026-02-09T12:22:08.593196
modified: 2026-02-19T22:30:36.478875
access URL: https://energydata.info/dataset/7e456b65-1c58-4031-9a91-397787c1334c/resource/7178b14e-f6fb-4023-8ab2-3fe1ffd7d3ab/download/healthcare_facilities.csv

Healthcare Facilities.zip
type: Distribution
format: ZIP
issued: 2026-02-09T12:21:38.256639
modified: 2026-02-19T22:30:36.478010
access URL: https://energydata.info/dataset/7e456b65-1c58-4031-9a91-397787c1334c/resource/c55d4ed7-238a-4029-8dbb-a65d7fdcd9b0/download/healthcare-facilities.zip

Healthcare_Facilities.json
type: Distribution
format: JSON
issued: 2026-02-09T12:21:53.707535
modified: 2026-02-19T22:30:36.478458
access URL: https://energydata.info/dataset/7e456b65-1c58-4031-9a91-397787c1334c/resource/f1faedc0-de28-4157-a48c-cb72d1482aed/download/healthcare_facilities.json

Global - Dams Database
type: Dataset
description: This database contains parameters gathered after an assessment of the technological potential for development of floating solar photovoltaic (PV) projects on existing hydropower dams and other reservoirs, starting with a pilot in FY18 focused on West Africa and India gathered from World Bank hydro projects. This database has been expanded to include the International Hydropower Association (IHA) dataset for India: https://energydata.info/dataset/india-hydropower-database It also includes data gathered from the FAO's public Aquastat database for Africa: http://www.fao.org/aquastat/en/databases/dams This dataset also contains data gathered from the public Global Reservoir and Dam Database (GRanD) for Africa.The database will be included in the Global Solar Atlas (http://globalsolaratlas.info/) where they will be directly linked to the solar resource through the Floating Solar module. The purpose of the resulting Floating Solar Atlas is to enable potential developers or stakeholders to quickly navigate to the site of interest and obtain information about the potential for development of a floating solar PV project. Following the FY19 updates, the database will continue to expand to other regions until a global dataset has been completed.The current coverage spans the continent of Africa and India only, with upcoming updates to include other continents.
issued: 2025-09-18T20:32:02.224677
modified: 2026-02-19T22:30:37.187889
contact point: Organization
  fn: Rachel Fox
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Global Dams Database Metadata
type: Distribution
format: XLSX
issued: 2026-02-06T17:18:54.048881
modified: 2026-02-19T22:30:37.191060
access URL: https://energydata.info/dataset/83d6a759-ed05-4f23-91ab-a8007d91adaa/resource/8a1e8ed8-f34b-4183-a7fb-f1d7a928a3cb/download/global-dams-database-2020_metadata_wbg-esmap.xlsx

Global Dams Database 2020
type: Distribution
format: CSV
issued: 2026-02-06T17:21:05.886692
modified: 2026-02-19T22:30:37.191514
access URL: https://energydata.info/dataset/83d6a759-ed05-4f23-91ab-a8007d91adaa/resource/e40c7995-672d-4a0c-93e7-afac29bcfb93/download/globaldamsdatabase_global_coverage_november_2020.csv

Europe and Central Asia - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).
issued: 2025-09-18T20:32:04.020503
modified: 2026-02-19T22:30:37.629623
contact point: Organization
  fn: Albertine Potter van Loon
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://globalwindatlas.info/

Europe and Central Asia - Wind Speed and Wind Power Potential Maps
type: Distribution
format: HTML
issued: 2026-02-09T13:50:00.508445
modified: 2026-02-19T22:30:37.632479
access URL: https://globalwindatlas.info/downloads/ECA

Zambia - Wind Measurement Data
type: Dataset
description: Data repository for measurements from 8 wind masts in Zambia. Data transmits daily reports for wind speed, wind direction, air pressure, relative humidity and temperature. Please refer to the country project page for additional outputs and reports, including installation reports: https://www.esmap.org/re-mapping/zambia. For access to maps and GIS layers, please visit the Global Wind Atlas: http://globalwindatlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP).
issued: 2025-09-18T20:32:06.784972
modified: 2026-02-19T22:30:38.125890
contact point: Organization
  fn: Oliver Knight
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Zambia - 12-month Site Resource Report
type: Distribution
format: PDF
issued: 2026-02-09T12:40:46.439610
modified: 2026-02-19T22:30:38.130134
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/10b51a7c-c082-46e4-8546-fea369ae1eef/download/renewable-energy-wind-mapping-for-zambia-12-month-site-resource-report.pdf

Wind-Measurements_Zambia_Mwinilunga_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:20:37.357236
modified: 2026-02-19T22:30:38.138924
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/12a30ef4-4a00-42f1-b6de-128d2b4de938/download/wind-measurements_zambia_mwinilunga_wb-esmap_header.xlsx

Wind-Measurements_Zambia_Petauke_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:21:50.450006
modified: 2026-02-19T22:30:38.140101
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/259f491d-c7ea-425e-9909-492f1f78d9d7/download/wind-measurements_zambia_petauke_wb-esmap_header.xlsx

Zambia - Wind Mapping Candidate Site Identification Report
type: Distribution
format: PDF
issued: 2026-02-09T12:40:21.102223
modified: 2026-02-19T22:30:38.129669
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/3853b2da-d707-4d1a-b583-a7185e91f188/download/111180-esm-p145271-public-zambiawindmappingcandidatesiteidentificationreport…

Wind-Measurements_Zambia_Mansa_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T13:17:25.719109
modified: 2026-02-19T22:30:38.137001
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/490c98bc-2552-44fd-aa93-56ebfbbbb4cf/download/wind-measurements_zambia_mansa_wb-esmap_raw.zip

Wind-Measurements_Zambia_Petauke_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:21:23.475000
modified: 2026-02-19T22:30:38.139724
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/4fd67946-b0cb-4628-9c47-c26378702818/download/wind-measurements_zambia_petauke_wb-esmap_qc.csv

Wind-Measurements_Zambia_Lusaka_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T13:02:58.274867
modified: 2026-02-19T22:30:38.134722
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/506bba5a-c37a-4413-a1e6-a1a72494ba54/download/wind-measurements_zambia_lusaka_wb-esmap_raw.zip

Wind-Measurements_Zambia_Mwinilunga_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T13:20:53.048044
modified: 2026-02-19T22:30:38.139322
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/5c404c62-ea23-4a07-a596-879edce26f6d/download/wind-measurements_zambia_mwinilunga_wb-esmap_raw.zip

Wind-Measurements_Zambia_Choma_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T12:55:37.199812
modified: 2026-02-19T22:30:38.133485
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/5f2f4ef3-9835-4a0b-bf49-e258d12b42a7/download/wind-measurements_zambia_choma_wb-esmap_raw.zip

Wind-Measurements_Zambia_Chanka_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T12:53:44.643247
modified: 2026-02-19T22:30:38.131803
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/6b7b4290-6fdc-4c81-95ae-d451778c2145/download/wind-measurements_zambia_chanka_wb-esmap_raw.zip

Wind-Measurements_Zambia_Malawi_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:04:42.621833
modified: 2026-02-19T22:30:38.135102
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/7526eac4-2848-4a7b-a61c-30695d7ff458/download/wind-measurements_zambia_mansa_wb-esmap_qc.csv

Wind-Measurements_Zambia_Mansa_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:16:35.850941
modified: 2026-02-19T22:30:38.136229
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/796dd268-2b28-42de-96c7-8dbc5d2dfb2f/download/wind-measurements_zambia_mansa_wb-esmap_qc.csv

Wind-Measurements_Zambia_Choma_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T12:54:34.893964
modified: 2026-02-19T22:30:38.132344
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/836e3a03-9be9-4b46-9666-a59fbb793139/download/wind-measurements_zambia_choma_wb-esmap_qc.csv

Wind-Measurements_Zambia_Chanka_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T12:52:27.588172
modified: 2026-02-19T22:30:38.130978
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/91a676c4-b2b3-4262-b6bc-b44b6c3b6523/download/wind-measurements_zambia_chanka_wb-esmap_qc.csv

Zambia - Wind Mapping Tower Commissioning Report
type: Distribution
format: PDF
issued: 2026-02-09T12:41:12.325366
modified: 2026-02-19T22:30:38.130557
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/993a61ca-168f-4552-bfe2-08c1357d2076/download/117433-esm-p145271-public-zambiawindmappingtowercommissioningreportswbesmapj…

Wind-Measurements_Zambia_Choma_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T12:55:08.758886
modified: 2026-02-19T22:30:38.133016
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/ac652b05-1539-46c6-95c6-89d9321481ce/download/wind-measurements_zambia_choma_wb-esmap_header.xlsx

Wind-Measurements_Zambia_Mansa_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:17:06.710602
modified: 2026-02-19T22:30:38.136609
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/ad79e01f-dcf9-4e9a-a1ec-0dcc51ca80f3/download/wind-measurements_zambia_mansa_wb-esmap_header.xlsx

Wind-Measurements_Zambia_Petauke_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T13:22:18.327400
modified: 2026-02-19T22:30:38.140514
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/b5c17780-4f7f-4af7-b5fb-2c7ba8eb5b8b/download/wind-measurements_zambia_petauke_wb-esmap_raw.zip

Wind-Measurements_Zambia_Mpika_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T13:18:31.482319
modified: 2026-02-19T22:30:38.138178
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/bbaf9f29-c269-43b6-99a4-a14660d25703/download/wind-measurements_zambia_mpika_wb-esmap_raw.zip

Wind-Measurements_Zambia_Malawi_WB-ESMAP_RAW
type: Distribution
format: ZIP
issued: 2026-02-09T13:06:02.356972
modified: 2026-02-19T22:30:38.135861
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/d27158b9-afc5-45c7-9e96-e192f4cfcc05/download/wind-measurements_zambia_malawi_wb-esmap_raw.zip

Wind-Measurements_Zambia_Chanka_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T12:53:04.941832
modified: 2026-02-19T22:30:38.131368
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/d9d3dc5d-ae68-4331-81d1-b665e0d8d108/download/wind-measurements_zambia_chanka_wb-esmap_header.xlsx

Wind-Measurements_Zambia_Mwinilunga_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:20:04.513434
modified: 2026-02-19T22:30:38.138556
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/db8533fc-d40c-4b92-9b43-dde766466043/download/wind-measurements_zambia_mwinilunga_wb-esmap_qc.csv

Wind-Measurements_Zambia_Mpika_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:18:13.214993
modified: 2026-02-19T22:30:38.137798
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/f6744771-4deb-4fe4-90d3-e196e69d774e/download/wind-measurements_zambia_mpika_wb-esmap_header.xlsx

Wind-Measurements_Zambia_Malawi_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:05:21.700182
modified: 2026-02-19T22:30:38.135484
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/f6cef61b-ccf9-49d8-a4b0-db49edc76e85/download/wind-measurements_zambia_mansa_wb-esmap_header.xlsx

Wind-Measurements_Zambia_Lusaka_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:02:38.084450
modified: 2026-02-19T22:30:38.134332
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/fa2103a5-7341-4b1f-907a-5900ba23f790/download/wind-measurements_zambia_lusaka_wb-esmap_header.xlsx

Wind-Measurements_Zambia_Mpika_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:17:45.506856
modified: 2026-02-19T22:30:38.137408
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/fd2d16e2-d4fa-4d09-9c20-21a888a303dd/download/wind-measurements_zambia_mpika_wb-esmap_qc.csv

Wind-Measurements_Zambia_Lusaka_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:00:19.117224
modified: 2026-02-19T22:30:38.133915
access URL: https://energydata.info/dataset/872905db-fded-4c08-8cc0-1a520092bac7/resource/ff3029a4-d047-4570-b315-97c83ca13e52/download/wind-measurements_zambia_lusaka_wb-esmap_qc.csv

Zambia - Solar Radiation Measurement Data
type: Dataset
description: Ground measurement data from 3 solar meteorological stations in Zambia. Data contains 1 minute average values for solar radiation, air temperature, relative humidity, barometric pressure, precipitation, wind speed and wind direction, cleaned and soiled radiance sensor (soiling measurement) and cleaning events.
issued: 2025-09-18T20:32:11.249964
modified: 2026-02-19T22:30:39.624309
contact point: Organization
  fn: Margot King
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Solar-Measurements_Zambia-Fig Tree_IFC_Header
type: Distribution
format: XLSX
issued: 2026-02-09T14:23:01.071179
modified: 2026-02-19T22:30:39.627442
access URL: https://energydata.info/dataset/8e156844-198a-4ddf-879e-25c69bccf25f/resource/391ae6bc-6254-49a8-abff-fe0fb154654d/download/solar-measurements_zambia-fig-tree_ifc_header.xlsx

Solar-Measurements_Zambia-Mumbwa_IFC_QC
type: Distribution
format: CSV
issued: 2026-02-09T14:25:05.800491
modified: 2026-02-19T22:30:39.628653
access URL: https://energydata.info/dataset/8e156844-198a-4ddf-879e-25c69bccf25f/resource/4fcaa686-47e8-400c-a955-ea8dfff6f73c/download/solar-measurements_zambia-mumbwa_ifc_qc.csv

Solar-Measurements_Zambia-Fig Tree_IFC_QC
type: Distribution
format: CSV
issued: 2026-02-09T14:24:16.254224
modified: 2026-02-19T22:30:39.627871
access URL: https://energydata.info/dataset/8e156844-198a-4ddf-879e-25c69bccf25f/resource/518a5486-af8b-41ab-8cf1-5cadb8d53e67/download/solar-measurements_zambia-fig-tree_ifc_qc.csv

Solar-Measurements_Zambia-Ndeke_IFC_Header
type: Distribution
format: XLSX
issued: 2026-02-09T14:25:35.349368
modified: 2026-02-19T22:30:39.629042
access URL: https://energydata.info/dataset/8e156844-198a-4ddf-879e-25c69bccf25f/resource/5dc96100-b6d1-4c32-8c98-f42cc6ba9c0f/download/solar-measurements_zambia-ndeke_ifc_header.xlsx

Solar-Measurements_Zambia-Mumbwa_IFC_Header
type: Distribution
format: XLSX
issued: 2026-02-09T14:24:51.655673
modified: 2026-02-19T22:30:39.628266
access URL: https://energydata.info/dataset/8e156844-198a-4ddf-879e-25c69bccf25f/resource/75b91c7e-35ae-4ad2-91fc-bf89cd9020a0/download/solar-measurements_zambia-mumbwa_ifc_header.xlsx

Solar-Measurements_Zambia-Ndeke_IFC_QC
type: Distribution
format: CSV
issued: 2026-02-09T14:25:50.155659
modified: 2026-02-19T22:30:39.629421
access URL: https://energydata.info/dataset/8e156844-198a-4ddf-879e-25c69bccf25f/resource/ea3de0ae-a9c9-4bee-8528-18e557c8e878/download/solar-measurements_zambia-ndeke_ifc_qc.csv

Kenya - Multi-Tier Framework (MTF) Survey
type: Dataset
description: The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (i.e. raw data sets in dta format), excel spreadsheet, pdf, and docx.
issued: 2025-09-18T20:32:13.344059
modified: 2026-02-19T22:30:40.146012
contact point: Organization
  fn: Alisha Pinto; Marina Brutinel; Bryan Bonsuk Koo; Dana Rysankova; Elisa Portale
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

MTF_KENYA_Questionnaire_Household.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:43:07.929483
modified: 2026-02-19T22:30:40.150320
access URL: https://energydata.info/dataset/8fe22b3d-5fbb-4af9-a50e-0730020eacfb/resource/30bc044d-82e2-47c3-8960-658548fb7739/download/mtf_kenya_questionnaire_household.pdf

MTF_Kenya_Energy_Access_Country_Diagnostic_Report_2020.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:42:37.402832
modified: 2026-02-19T22:30:40.149337
access URL: https://energydata.info/dataset/8fe22b3d-5fbb-4af9-a50e-0730020eacfb/resource/6963aec8-fa94-41d4-9c5e-cb3582047ea7/download/mtf-energy-access-country-diagnostics-report_kenya.pdf

RAW DATA.zip
type: Distribution
format: ZIP
issued: 2026-02-09T13:43:37.645170
modified: 2026-02-19T22:30:40.151174
access URL: https://energydata.info/dataset/8fe22b3d-5fbb-4af9-a50e-0730020eacfb/resource/9fd8053b-8ce0-4731-8a4b-dd062089bccd/download/raw-data.zip

MTF_Household_Sampling_Strategy.docx
type: Distribution
format: DOC
issued: 2026-02-09T13:43:22.023760
modified: 2026-02-19T22:30:40.150752
access URL: https://energydata.info/dataset/8fe22b3d-5fbb-4af9-a50e-0730020eacfb/resource/aa888b57-b0f2-44f8-916d-b3ee65793e00/download/mtf_household_sampling_strategy.docx

Codebook_KENYA.xlsx
type: Distribution
format: XLSX
issued: 2026-02-09T13:42:53.276482
modified: 2026-02-19T22:30:40.149866
access URL: https://energydata.info/dataset/8fe22b3d-5fbb-4af9-a50e-0730020eacfb/resource/fa990cbe-d243-4f75-b184-95ac74cd1487/download/codebook_kenya.xlsx

Republic of Korea - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed, wind rose and wind power density potential in The Republic of Korea. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
issued: 2026-01-22T22:16:18.367912
modified: 2026-02-19T22:30:43.739026
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Republic of Korea - Wind Speed and Wind Power Potential Maps - GIS
type: Distribution
format: HTML
issued: 2026-02-09T14:12:52.168981
modified: 2026-02-19T22:30:43.742252
access URL: https://globalwindatlas.info/area/Republic%20of%20Korea

Republic of Korea - Wind Speed and Wind Power Potential Maps - Posters
type: Distribution
format: HTML
issued: 2026-02-09T14:12:18.270740
modified: 2026-02-19T22:30:43.741828
access URL: https://globalwindatlas.info/downloads/RepublicofKorea

Bangladesh - Wind Measurement Data
type: Dataset
description: Data repository for measurements from a wind measurement station with a lidar in Bangladesh. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values. Please refer to the country project page for additional outputs and reports, including the installation reports: http://esmap.org/re-mapping/bangladesh. For access to maps and GIS layers, please visit the Global Wind Atlas: https://globalwindatlas.info/
issued: 2025-09-18T20:32:18.913651
modified: 2026-02-19T22:30:44.030463
contact point: Organization
  fn: Clara Ivanescu
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://globalwindatlas.info/

Wind-Measurements_Bangladesh_Feni_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:52:30.134139
modified: 2026-02-19T22:30:44.034863
access URL: https://energydata.info/dataset/946082a5-9ee1-48dc-ac5a-5dc49ebd4d34/resource/54913f41-9ca9-4279-81f8-9b4d1130f932/download/wind-measurements_bangladesh_feni_wb-esmap_qc.csv

Wind-Measurements_Bangladesh_Feni_WB-ESMAP_Header
type: Distribution
format: CSV
issued: 2026-02-09T13:53:29.127068
modified: 2026-02-19T22:30:44.035667
access URL: https://energydata.info/dataset/946082a5-9ee1-48dc-ac5a-5dc49ebd4d34/resource/5bd5fdaf-7156-4eb7-b373-bcf6889f981a/download/wind-measurements_bangladesh_feni_wb-esmap_header.csv

Bangladesh - Solar Wind Measurements Implementation Plan
type: Distribution
format: PDF
issued: 2026-02-09T13:51:41.790818
modified: 2026-02-19T22:30:44.033922
access URL: https://energydata.info/dataset/946082a5-9ee1-48dc-ac5a-5dc49ebd4d34/resource/63cbb270-e855-42bb-b075-eb84e436c44a/download/bangladesh-solarwindmeasurementsimplementationplan.pdf

Wind-Measurements_Monthly-Reports_Bangladesh_Feni_WB-ESMAP
type: Distribution
format: ZIP
issued: 2026-02-09T13:54:04.356928
modified: 2026-02-19T22:30:44.036124
access URL: https://energydata.info/dataset/946082a5-9ee1-48dc-ac5a-5dc49ebd4d34/resource/9e274566-8a23-43e3-85c9-d532e4039c8e/download/wind-measurements-monthly-reports_bangladesh_feni_wb-esmap.zip

Bangladesh - Solar Wind Measurements Installation Report
type: Distribution
format: PDF
issued: 2026-02-09T13:52:10.905825
modified: 2026-02-19T22:30:44.034398
access URL: https://energydata.info/dataset/946082a5-9ee1-48dc-ac5a-5dc49ebd4d34/resource/e27b7279-517c-42ea-822e-ad8e9e1c4752/download/bangladesh-solarwindmeasurementsinstallationreport.pdf

Wind-Measurements_Bangladesh_Feni_WB-ESMAP_Raw
type: Distribution
format: ZIP
issued: 2026-02-09T13:53:06.597061
modified: 2026-02-19T22:30:44.035276
access URL: https://energydata.info/dataset/946082a5-9ee1-48dc-ac5a-5dc49ebd4d34/resource/e509f61a-5302-43de-bb5a-e08803217734/download/wind-measurements_bangladesh_feni_wb-esmap_raw.zip

Kenya - Power Stations
type: Dataset
description: The dataset contains location of Power Stations in Kenya. It was provided by Kenya Power and Lighting Company (KPLC).
issued: 2025-09-18T20:32:23.214349
modified: 2026-02-19T22:30:44.916064
contact point: Organization
  fn: Rhonda Jordan Antoine; Patrick Balla; Laurencia Njagi; Sudeshna Banerjee.
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://www.kplc.co.ke/

Power Stations
type: Distribution
format: ZIP
issued: 2026-02-09T12:19:18.188525
modified: 2026-02-19T22:30:44.918930
access URL: https://energydata.info/dataset/9a215d6f-7dce-410e-86ba-80ca8aafd250/resource/18e16fac-d0b9-47cb-9f8c-23fafd4649ee/download/power-stations.zip

Power Stations
type: Distribution
format: JSON
issued: 2026-02-09T12:19:32.551154
modified: 2026-02-19T22:30:44.919360
access URL: https://energydata.info/dataset/9a215d6f-7dce-410e-86ba-80ca8aafd250/resource/1b5d7fc0-acad-4d1e-95c3-4bffaf978b31/download/power_stations.json

Pakistan – Biomass Mapping
type: Dataset
description: The GIS Biomass Atlas of Pakistan is the final output from the biomass resource mapping component of the activity “ Renewable Energy Resource Mapping and Geospatial Planning – Pakistan” [Project ID: P146140]. You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan To visualize the geospatial data generated in this study, please access: https://irena.masdar.ac.ae/GIS/?map=2636 Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass GIS Atlas, 2016, https://energydata.info/dataset/pakistan-biomass-gis-atlas"
issued: 2025-09-18T20:32:41.224620
modified: 2026-02-19T22:30:48.960556
contact point: Organization
  fn: Clara Ivanescu
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Pakistan - Biomass Feedstock Crop Yield
type: Distribution
format: CSV
issued: 2026-02-09T14:07:27.300412
modified: 2026-02-19T22:30:48.965721
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/07c45076-4b97-4d61-8b16-caff05d3ce28/download/pakistanbiomassfeedstockcropyield.csv

Pakistan - Biomass Land Use
type: Distribution
format: ZIP
issued: 2026-02-09T14:07:44.473290
modified: 2026-02-19T22:30:48.966201
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/080ec529-6467-469d-ba4b-11e465061b38/download/pakbiomasslandusewbesmap.zip

Pakistan - Sugar Mills
type: Distribution
format: GeoJSON
issued: 2026-02-09T14:09:31.625314
modified: 2026-02-19T22:30:48.967787
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/1adb6eb5-61e7-49e2-8d15-f580686db045/download/pksugarmills.geojson

Pakistan - Biomass Atlas
type: Distribution
format: ZIP
issued: 2026-02-09T14:06:37.899008
modified: 2026-02-19T22:30:48.964741
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/1d140d65-53a4-4885-9772-ed367c644ee9/download/pakbiomassatlaswbesmap.zip

Pakistan - Biomass Industrial
type: Distribution
format: CSV
issued: 2026-02-09T14:06:11.987538
modified: 2026-02-19T22:30:48.964270
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/5470dfd7-0e59-4c26-b5f4-dc9f1c3221b7/download/pakistanbiomassindustrial.csv

Pakistan - Rice Mills
type: Distribution
format: GeoJSON
issued: 2026-02-09T14:09:10.490324
modified: 2026-02-19T22:30:48.967408
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/6dfbde88-90c3-4480-be10-071b34655fc3/download/pkricemills.geojson

Pakistan - Diary Farms
type: Distribution
format: GeoJSON
issued: 2026-02-09T14:08:25.392980
modified: 2026-02-19T22:30:48.966618
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/8272f8d2-c501-4184-9300-ef6bc8247466/download/pkdairyfarms.geojson

Pakistan - MSW
type: Distribution
format: GeoJSON
issued: 2026-02-09T14:08:50.674962
modified: 2026-02-19T22:30:48.967017
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/da8b0377-4ccf-4f13-b4b4-3857169c8fe4/download/pkmunicipalsolidwaste.geojson

Pakistan - Biomass Field Survey
type: Distribution
format: CSV
issued: 2026-02-09T14:07:12.277867
modified: 2026-02-19T22:30:48.965232
access URL: https://energydata.info/dataset/a27a9b60-706b-4c81-8608-c913d2ed998f/resource/ff81c75f-bc41-4d36-903f-3bdca3ad754e/download/pakistanbiomassfieldsurvey.csv

Vietnam - Solar Radiation Measurement Data
type: Dataset
description: Data repository for measurements from 5 automated solar stations in Vietnam. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values for solar radiation levels, temperature, air pressure and wind speed. For more information and additional outputs, please visit: http://esmap.org/re_mapping_vietnam For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ 5 stations: VNCEH (Central Highlands): Tier 1 HelioScale omega / VNDAN (Da Nang): Tier 2 HelioScale phi / VNHAN (Hanoi): Tier 1 HelioScale omega / VNSOB (Song Binh): Tier 2 HelioScale phi / VNTRA (Tri-An): Tier 1 HelioScale omega Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Vietnam-Solar Radiation Measurement Data, 2017,
issued: 2025-09-18T20:32:44.518784
modified: 2026-02-19T22:30:49.901589
contact point: Organization
  fn: Joana Zerbin
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://esmap.org/re_mapping_vietnam

Solar-Measurements_Vietnam_SongBinh_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T11:16:27.409066
modified: 2026-02-19T22:30:49.906161
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/15966538-27d9-48d6-8464-078cb368094a/download/solar-measurements_vietnam_songbinh_wb-esmap_qc.csv

Solar-Measurements_Vietnam_TriAn_WB-ESMAP_Header
type: Distribution
format: CSV
issued: 2026-02-09T11:30:22.252944
modified: 2026-02-19T22:30:49.910355
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/198ccbf2-859c-41ca-a951-5c4197ba04af/download/solar-measurements_vietnam_trian_wb-esmap_header.csv

Solar-Measurements_Vietnam_CentralHighlands_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T11:11:26.596731
modified: 2026-02-19T22:30:49.904900
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/34e7c4cb-8704-42d3-a70c-a74a08db773a/download/solar-measurements_vietnam_centralhighlands_wb-esmap_qc.csv

Solar-Measurements_Monthly-Reports_Vietnam_DaNang_WB-ESMAP
type: Distribution
format: ZIP
issued: 2026-02-09T11:32:18.025814
modified: 2026-02-19T22:30:49.911120
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/3965a02d-419b-4553-94cf-ea49b4384bf1/download/solar-measurements_monthly-report_vietnam_danang_wb-esmap.zip

Solar-Measurements_Vietnam_TriAn_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T11:19:20.579894
modified: 2026-02-19T22:30:49.906550
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/6baaf42d-8cc3-4938-bf5b-a3da55b2ede2/download/solar-measurements_vietnam_trian_wb-esmap_qc.csv

Solar-Measurements_Vietnam_Hanoi_WB-ESMAP_Header
type: Distribution
format: CSV
issued: 2026-02-09T11:28:20.704697
modified: 2026-02-19T22:30:49.908804
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/6f85832c-f577-4402-aaf7-21cecb22fc9f/download/solar-measurements_vietnam_hanoi_wb-esmap_header.csv

Solar-Measurements-Monthly-Reports_Vietnam_TriAn_WB-ESMAP
type: Distribution
format: ZIP
issued: 2026-02-09T11:33:56.405177
modified: 2026-02-19T22:30:49.912223
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/79f728b3-8996-4cf1-ac88-9d330bf693d5/download/solar-measurements-monthly-report_vietnam_trian_wb-esmap.zip

Solar-Measurements_Vietnam_TriAn_WB-ESMAP_Raw
type: Distribution
format: CSV
issued: 2026-02-09T11:27:29.453481
modified: 2026-02-19T22:30:49.908438
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/84aacff1-a72f-48ff-a85c-e480a4d9f2c2/download/solar-measurements_vietnam_trian_wb-esmap_raw.zip

Solar-Measurements_Vietnam_DaNang_WB-ESMAP_Raw
type: Distribution
format: ZIP
issued: 2026-02-09T11:22:22.208642
modified: 2026-02-19T22:30:49.907346
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/b10535c0-1a87-4513-b4c2-97bf4e8d5427/download/solar-measurements_vietnam_danang_wb-esmap_raw.zip

Solar-Measurements-Monthly-Reports_Vietnam_Hanoi_WB-ESMAP
type: Distribution
format: ZIP
issued: 2026-02-09T11:32:45.144598
modified: 2026-02-19T22:30:49.911502
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/bb45effd-e3e5-47e8-86ec-6a064aec4cfd/download/solar-measurements-monthly-report_vietnam_hanoi_wb-esmap.zip

Solar-Measurements_Vietnam_SongBinh_WB-ESMAP_Header
type: Distribution
format: CSV
issued: 2026-02-09T11:30:07.024812
modified: 2026-02-19T22:30:49.909988
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/be39f9bf-709a-44db-87d3-b78789dfff0b/download/solar-measurements_vietnam_songbinh_wb-esmap_header.csv

Solar-Measurements_Vietnam_DaNang_WB-ESMAP_Header
type: Distribution
format: CSV
issued: 2026-02-09T11:29:46.227434
modified: 2026-02-19T22:30:49.909619
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/c62dc4f2-51fd-403d-9b05-2cfe28e02ecf/download/solar-measurements_vietnam_danang_wb-esmap_header.csv

Solar-Measurements-Monthly-Reports_Vietnam_Songbinh_WB_ESMAP
type: Distribution
format: ZIP
issued: 2026-02-09T11:33:19.498818
modified: 2026-02-19T22:30:49.911865
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/ca2bd0b3-8f21-430b-83be-915333283012/download/solar-measurements-monthly-report_vietnam_songbinh_wb-esmap.zip

Solar-Measurements_Vietnam_Hanoi_WB-ESMAP_Raw
type: Distribution
format: ZIP
issued: 2026-02-09T11:23:45.663021
modified: 2026-02-19T22:30:49.907715
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/caf63b5d-51ee-4748-9175-bf9a925e3f5b/download/solar-measurements_vietnam_bacninh_wb-esmap_raw.zip

Solar-Measurements_Vietnam_DaNang_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T11:13:17.202003
modified: 2026-02-19T22:30:49.905329
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/d0b2d84b-8aa0-4be2-9c08-d57c36d580b4/download/solar-measurements_vietnam_danang_wb-esmap_qc.csv

olar-Measurements_Vietnam_CentralHighlands_WB-ESMAP_Raw
type: Distribution
format: ZIP
issued: 2026-02-09T11:21:08.795021
modified: 2026-02-19T22:30:49.906946
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/d2f8bb10-2ddd-41f6-9492-f8374af9c408/download/solar-measurements_vietnam_centralhighlands_wb-esmap_raw.zip

Solar-Measurements_Vietnam_Hanoi_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T11:15:12.017467
modified: 2026-02-19T22:30:49.905746
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/d8f60c9a-3ee6-4605-896b-5e90a1ee2e5c/download/solar-measurements_vietnam_bacninh_wb-esmap_qc.csv

Solar-Measurements_Vietnam_SongBinh_WB-ESMAP_Raw
type: Distribution
format: ZIP
issued: 2026-02-09T11:26:25.215551
modified: 2026-02-19T22:30:49.908075
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/e8858a09-2963-4e53-aaf9-0f71da0ed191/download/solar-measurements_vietnam_songbinh_wb-esmap_raw.zip

Solar-Measurements_Monthly-Reports_Vietnam_CentralHighlands_WB-ESMAP
type: Distribution
format: ZIP
issued: 2026-02-09T11:31:35.669696
modified: 2026-02-19T22:30:49.910718
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/e9a0b606-b0ff-4c96-b05a-78b07a4121d5/download/solar-measurements_monthly-report_vietnam_centralhighlands_wb-esmap.zip

Solar-Measurements_Vietnam_CentralHighlands_WB-ESMAP_Header
type: Distribution
format: CSV
issued: 2026-02-09T11:29:13.708661
modified: 2026-02-19T22:30:49.909207
access URL: https://energydata.info/dataset/a50bd1ba-49f0-44be-a134-413f969d4376/resource/f6f45fc8-f3a5-4b4f-97fb-9b15c89d532d/download/solar-measurements_vietnam_centralhighlands_wb-esmap_header.csv

Unites States of America - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed, wind rose and wind power density potential in The United States of America. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
issued: 2026-01-22T22:16:28.219761
modified: 2026-02-19T22:30:50.385062
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

United States of America - Wind Speed and Wind Power Potential Maps - Posters
type: Distribution
format: HTML
issued: 2026-02-09T14:19:49.786255
modified: 2026-02-19T22:30:50.388324
access URL: https://globalwindatlas.info/downloads/UnitedStatesofAmerica

United States of America - Wind Speed and Wind Power Potential Maps - GIS
type: Distribution
format: HTML
issued: 2026-02-09T14:19:23.280638
modified: 2026-02-19T22:30:50.387887
access URL: https://globalwindatlas.info/area/United%20States%20of%20America

The Netherlands - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed, wind rose and wind power density potential in The Netherlands. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
issued: 2026-01-22T22:16:30.320748
modified: 2026-02-19T22:30:51.080535
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

The Netherlands - Wind Speed and Wind Power Potential Maps - GIS
type: Distribution
format: HTML
issued: 2026-02-09T14:15:36.181262
modified: 2026-02-19T22:30:51.083430
access URL: https://globalwindatlas.info/area/Netherlands

The Netherlands - Wind Speed and Wind Power Potential Maps - Posters
type: Distribution
format: HTML
issued: 2026-02-09T14:15:57.491308
modified: 2026-02-19T22:30:51.083855
access URL: https://globalwindatlas.info/downloads/Netherlands

Afghanistan - Global Horizontal Irradiation
type: Dataset
description: Global Horizontal Irradiation (GHI) raster file over the territory of Afghanistan. Aggregated annual average values. Spatial resolution 5km.
issued: 2025-09-18T20:32:51.590179
modified: 2026-02-19T22:30:51.719115
contact point: Organization
  fn: Alexandros Korkovelos
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: The World Bank

Afghanistan GHI
type: Distribution
format: TIFF
issued: 2026-02-09T12:25:15.905065
modified: 2026-02-19T22:30:51.722050

Philippines - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed, wind rose and wind power density potential in The Philippines. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
issued: 2025-09-18T20:32:57.399118
modified: 2026-02-19T22:30:52.863426
contact point: Organization
  fn: Albertine Potter van Loon
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://globalwindatlas.info

Philippines - Wind Speed and Wind Power Potential Maps - GIS
type: Distribution
format: HTML
issued: 2026-02-09T11:42:41.855173
modified: 2026-02-19T22:30:52.866326
access URL: https://globalwindatlas.info/area/Philippines

Philippines - Wind Speed and Wind Power Potential Maps - Posters
type: Distribution
format: HTML
issued: 2026-02-09T11:43:53.055850
modified: 2026-02-19T22:30:52.866743
access URL: https://globalwindatlas.info/downloads/Philippines

Latin America and Caribbean - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed and wind power density potential in Latin America and Caribbean. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).
issued: 2025-09-18T20:32:59.195574
modified: 2026-02-19T22:30:53.373607
contact point: Organization
  fn: Albertine Potter van Loon
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://globalwindatlas.info/

Latin America and Caribbean - Wind Speed and Wind Power Potential Maps
type: Distribution
format: HTML
issued: 2026-02-09T12:20:48.735459
modified: 2026-02-19T22:30:53.376467
access URL: https://globalwindatlas.info/downloads/LCA

Afghanistan - Cities
type: Dataset
description: The data shows the location of cities in Afghanistan.
issued: 2025-09-18T20:33:05.586890
modified: 2026-02-19T22:30:54.451304
contact point: Organization
  fn: Alexandros Korkovelos
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Cities in Afghanistan
type: Distribution
format: ZIP
issued: 2026-02-09T12:26:10.139007
modified: 2026-02-19T22:30:54.454230
access URL: https://energydata.info/dataset/c1c0110b-fe40-44e9-8b4d-bb6aa2747b0b/resource/152f64f6-5b1e-4892-a1fa-ec199c0e4fcc/download/cities.zip

Cities in Afghanistan
type: Distribution
format: GeoJSON
issued: 2026-02-09T12:26:30.151716
modified: 2026-02-19T22:30:54.454661
access URL: https://energydata.info/dataset/c1c0110b-fe40-44e9-8b4d-bb6aa2747b0b/resource/6dc01bc0-42d7-4c5c-a685-fe773d11572b/download/afghancitieswgs84.geojson

R manual for remote sensing analysis
type: Dataset
description: Manual and scripts in R for conducting “agnostic” image classification and accuracy assessment.
issued: 2026-02-11T15:29:01.459395
modified: 2026-02-11T15:29:01.459400
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

R manual for remote sensing analysis
type: Distribution
format: HTML
issued: 2021-11-03T02:07:14.546490
modified: 2026-02-11T15:29:01.452046
access URL: https://remotesensing.montana.edu/r-manual.html

Landsat Collections Poster
type: Dataset
description: 30" x 20" poster about Landsat Collections
issued: 2026-02-11T15:29:02.204078
modified: 2026-02-11T15:29:02.204084
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Landsat Collections Poster
type: Distribution
format: PDF
issued: 2021-09-16T19:04:33.292102
modified: 2026-02-11T15:29:02.196268
access URL: https://ckan.americaview.org/data/UtahView/UtahView_2Landsat_Collections_20210915.pdf

Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
type: Dataset
description: The capacity of terrestrial ecosystems to sequester carbon dioxide (CO2) from the atmosphere is expected to be altered by climate change and CO2 fertilization, but this projection is limited by our understanding of how the soil system interacts with plants. Understanding the soil–vegetation interactions is essential to assess the magnitude and response of terrestrial ecosystems to the changing climate. Here, we used soil profile and satellite data to explore the role that soil properties play in regulating water and carbon use by plants. Data obtained for 19 terrestrial ecosystem sites in a warm temperate and humid climate were used to investigate the relationship between remotely sensed data and soil physical and chemical properties. Classification and regression tree results showed that in situ soil carbon isotope (δ13C), and soil order were significant predictors (r2 = 0.39, mean absolute error (MAE) = 0 of 0.175 gC/KgH2O) of remotely sensed water use efficiency (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS). Soil extractable calcium (Ca), and land cover type were significant predictors of remotely sensed carbon use efficiency (CUE) based on MODIS and Landsat data-(r2 = 0.64–0.78, MAE = 0.04–0.06). We used gross primary productivity (GPP) derived from solar-induced fluorescence (SIF) data, based on the Orbiting Carbon Observatory-2 (OCO-2), to calculate WUE and CUE (referred to as WUESIF and CUESIF, respectively) for our study sites. The regression tree analysis revealed that soil organic matter and soil extractable magnesium (Mg), δ13C, and soil silt content were the important predictors of both WUESIF (r2 = 0.19, MAE = 0.64 gC/KgH2O) and CUESIF (r2 = 0.45, MAE = 0.1), respectively. Our results revealed the importance of soil extractable Ca, soil carbon (S13C is a facet of soil carbon content), and soil organic matter predicting CUE and WUE. Insights gained from this study highlighted the importance of biotic and abiotic factors regulat…
issued: 2026-02-11T15:29:03.055202
modified: 2026-02-11T15:29:03.055206
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Journal
type: Distribution
issued: 2023-07-28T15:01:39.783595
modified: 2026-02-11T15:29:03.047883
access URL: https://doi.org/10.3390/rs13132593

GIS Foundation course on edX.org
type: Dataset
description: Learn foundations of GIS concepts and geospatial technology to view, understand, query, visualize, and interpret geospatial data to reveal patterns and relationships for effective problem-solving and better decision making.
issued: 2026-02-11T15:29:03.827164
modified: 2026-02-11T15:29:03.827168
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Website
type: Distribution
issued: 2023-08-14T18:14:17.504405
modified: 2026-02-11T15:29:03.819629
access URL: https://www.edx.org/course/gis-foundations

Landsat Products Poster
type: Dataset
description: 30" x 20" poster about Landsat products (Level-1 data products, Level-2 science products, and Level-3 science products)
issued: 2026-02-11T15:29:04.606770
modified: 2026-02-11T15:29:04.606775
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Landsat Products Poster
type: Distribution
format: PDF
issued: 2021-09-16T19:06:07.101178
modified: 2026-02-11T15:29:04.598242
access URL: https://ckan.americaview.org/data/UtahView/UtahView_3Landsat_Products_20210915.pdf

From pixel to yield: forecasting potato productivity in Lebanon and Idaho
type: Dataset
description: Idaho and Lebanon rely on potatoes as an economically important crop. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) indices were calculated from PlanetScope satellite imagery for the 2017 growing season cloud free days. Variations in vegetation health were tracked over time and correlated to yield data provided by growers in Idaho. Based on ordinary least squares regression an Idaho yield forecast model was developed. Vegetation response during the growth stage at which potato tubers were filling out was significant in predicting yield for both the Norkotah and Russet potato variety. This corresponded to a week with high recorded temperatures that impacted the health status of the crops. The yield forecasting model was validated with a cross validation approach and then applied to potato fields in Lebanon. The Idaho model successfully displayed yield variation in crops for Lebanon. Spectral indices along with field topography allow the prediction of yield based on the crop type and variety.
issued: 2026-02-11T15:29:05.316801
modified: 2026-02-11T15:29:05.316806
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 1–7, 2020
type: Distribution
issued: 2021-09-16T17:28:02.252338
modified: 2026-02-11T15:29:05.307362
access URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W11/1/2020/

ArkansasView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and successes of ArkansasView.
issued: 2026-02-11T15:29:06.108556
modified: 2026-02-11T15:29:06.108560
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

ArkansasView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T13:19:39.789951
modified: 2026-02-11T15:29:06.099976
access URL: https://ckan.americaview.org/data/factsheets/ArkansasView_2020_Factsheet.pdf

ArkansasView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-08-27T14:46:34.729451
modified: 2026-02-11T15:29:06.098773
access URL: https://ckan.americaview.org/data/factsheets/ArkansasView_2015_Factsheet.pdf

ArkansasView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-08-27T14:45:56.462102
modified: 2026-02-11T15:29:06.098325
access URL: https://ckan.americaview.org/data/factsheets/ArkansasView_2014_Factsheet.pdf

ArkansasView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-08-27T14:47:44.978834
modified: 2026-02-11T15:29:06.099589
access URL: https://ckan.americaview.org/data/factsheets/ArkansasView_2019_Factsheet.pdf

ArkansasView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-09T14:31:35.763859
modified: 2026-02-11T15:29:06.100366
access URL: https://ckan.americaview.org/data/factsheets/GY21_Arkansas_Factsheet.pdf

ArkansasView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-21T19:34:05.984917
modified: 2026-02-11T15:29:06.100777
access URL: https://ckan.americaview.org/data/factsheets/GY22_AR_Factsheet.pdf

ArkansasView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-03T16:08:10.479594
modified: 2026-02-11T15:29:06.101197
access URL: https://ckan.americaview.org/data/factsheets/GY23_AR_Factsheet.pdf

ArkansasView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-08-27T14:47:12.231213
modified: 2026-02-11T15:29:06.099189
access URL: https://ckan.americaview.org/data/factsheets/ArkansasView_2018_Factsheet.pdf

MinnesotaView Factsheets
type: Dataset
description: Yearly factsheets that highlight the efforts and success of MinnesotaView.
issued: 2026-02-11T15:29:07.162163
modified: 2026-02-11T15:29:07.162166
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

MinnesotaView 2020 Factsheet
type: Distribution
format: PDF
issued: 2022-04-13T02:53:38.076978
modified: 2026-02-11T15:29:07.153996
access URL: https://ckan.americaview.org/data/factsheets/MinnesotaView_2020_Factsheet.pdf

MinnesotaView 2015 Factsheet
type: Distribution
format: PDF
issued: 2021-09-14T01:47:25.156711
modified: 2026-02-11T15:29:07.152841
access URL: https://ckan.americaview.org/data/factsheets/MinnesotaView_2015_Factsheet.pdf

MinnesotaView 2022 Factsheet
type: Distribution
format: PDF
issued: 2024-02-22T16:48:30.283161
modified: 2026-02-11T15:29:07.154738
access URL: https://ckan.americaview.org/data/factsheets/GY22_MN_Factsheet.pdf

MinnesotaView 2019 Factsheet
type: Distribution
format: PDF
issued: 2021-09-14T01:48:30.165408
modified: 2026-02-11T15:29:07.153622
access URL: https://ckan.americaview.org/data/factsheets/MinnesotaView_2019_Factsheet.pdf

MinnesotaView 2021 Factsheet
type: Distribution
format: PDF
issued: 2023-02-28T19:29:04.318976
modified: 2026-02-11T15:29:07.154365
access URL: https://ckan.americaview.org/data/factsheets/GY21_Minnesota_Factsheet.pdf

MinnesotaView 2014 Factsheet
type: Distribution
format: PDF
issued: 2021-09-14T01:46:54.366088
modified: 2026-02-11T15:29:07.152420
access URL: https://ckan.americaview.org/data/factsheets/MinnesotaView_2014_Factsheet.pdf

MinnesotaView 2018 Factsheet
type: Distribution
format: PDF
issued: 2021-09-14T01:47:51.547887
modified: 2026-02-11T15:29:07.153238
access URL: https://ckan.americaview.org/data/factsheets/MinnesotaView_2018_Factsheet.pdf

2021 UAS Team Projects
type: Dataset
description: Throughout 2021, the Unoccupied Aircraft System (UAS) Team at the University of Vermont Spatial Analysis Lab carried out a variety of missions and projects, ranging from individual flight operations to multiple missions for ongoing partnerships and grants. Additional projects included research, training sessions, and professional workshops as well.
issued: 2026-02-11T15:29:07.964527
modified: 2026-02-11T15:29:07.964530
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

StoryMap
type: Distribution
issued: 2023-03-03T20:31:21.069876
modified: 2026-02-11T15:29:07.957109
access URL: https://storymaps.arcgis.com/stories/16ec77390e1741bca2e5798f1aaeb624

New YorkView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of New YorkView.
issued: 2026-02-11T15:29:08.612997
modified: 2026-02-11T15:29:08.613001
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

New YorkView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:20:50.166367
modified: 2026-02-11T15:29:08.602980
access URL: https://ckan.americaview.org/data/factsheets/New_YorkView_2014_Factsheet.pdf

New YorkView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-06T20:09:29.898803
modified: 2026-02-11T15:29:08.605713
access URL: https://ckan.americaview.org/data/factsheets/GY23_NY_Factsheet.pdf

New YorkView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T17:43:21.208962
modified: 2026-02-11T15:29:08.605322
access URL: https://ckan.americaview.org/data/factsheets/GY22_NY_Factsheet.pdf

New YorkView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-28T19:38:57.584593
modified: 2026-02-11T15:29:08.604933
access URL: https://ckan.americaview.org/data/factsheets/GY21_NewYork_Factsheet.pdf

New YorkView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:23:22.118044
modified: 2026-02-11T15:29:08.604173
access URL: https://ckan.americaview.org/data/factsheets/New_YorkView_2019_Factsheet.pdf

New YorkView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:22:19.581319
modified: 2026-02-11T15:29:08.603799
access URL: https://ckan.americaview.org/data/factsheets/New_YorkView_2018_Factsheet.pdf

New YorkView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T13:24:44.742486
modified: 2026-02-11T15:29:08.604541
access URL: https://ckan.americaview.org/data/factsheets/New_YorkView_2020_Factsheet.pdf

New YorkView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:21:48.762925
modified: 2026-02-11T15:29:08.603410
access URL: https://ckan.americaview.org/data/factsheets/New_YorkView_2015_Factsheet.pdf

Is high spatial resolution DEM data necessary for mapping palustrine wetlands?
type: Dataset
description: Digital elevation models (DEMs) have been found to be an effective data source for automated mapping of wetlands. However, it is unclear whether high spatial resolution DEMs, which tend to be more expensive to acquire and process, are necessary for mapping wetlands such as those in the US National Wetland Inventory (NWI). Therefore, we compared predictions of the probability of palustrine wetland occurrence with a random forests (RF) algorithm using DEMs generated from light detection and ranging (LiDAR) at 1 m, 3 m, and 10 m raster cell sizes; and photogrammetrically-derived DEMs at 3 m and 10 m. For each classification, a wide range of terrain derivatives were generated and used as the input data for the classification. Comparisons between the wetland predictions were made using the receiver operating characteristic (ROC) area under the curve (AUC) measure, the Kappa statistic, overall accuracy, class user’s and producer’s accuracy, and the out of bag (OOB) error rate. For two different study sites, irrespective of the source of the digital terrain data, palustrine wetland occurrence was predicted with AUC values greater than 0.95, overall accuracies greater than 88%, Kappa greater than 0.77, and wetland user’s and producer’s accuracies above 0.85 when using a large training data set derived from the NWI or a small separate data set of non-NWI data derived from field samples. We therefore conclude that the source (LiDAR vs photogrammetric) and spatial scale (1 m, 3 m, or 10 m) of the DEM data does not have a large impact on the accuracy of the prediction of wetlands such as those in the NWI. However, for small wetlands, or more generally for wetlands unlike those in the NWI, finer scale data (e.g. 1 m) derived from LiDAR may be preferable.
issued: 2026-02-11T15:29:09.653459
modified: 2026-02-11T15:29:09.653464
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

International Journal of Remote Sensing Volume 40, 2019 - Issue 1
type: Distribution
format: HTML
issued: 2021-09-17T21:36:07.036386
modified: 2026-02-11T15:29:09.645951
access URL: https://www.tandfonline.com/doi/abs/10.1080/01431161.2018.1506184

Modeling and Visualizing Ecosystem Services for 3D Urban Planning
type: Dataset
description: This study integrated ecosystem services (ES) and 3D geo-visualization with urban design practices to inform future development in Pocatello, Idaho. Pocatello is a medium-sized city in southeastern Idaho that faces a range of challenges for sustainable future growth. It is important to understand what different urban designs can contribute to local ES. The objectives for this study were: building a 3D model for Pocatello Old Town, integrating key attributes in the model for planning and geodesign, visualizing in 3D using VR, and WebGL, and further, creating and analyzing the changes on local ES via future scenarios.The results showed that planting more treesand replacing impervious parking lots with green parking lots could reduce land surface temperature and improve air quality by removing particulate matter. With this approach, the decision makers in the City of Pocatello can analyze future scenarios both visually and statistically.
issued: 2026-02-11T15:29:10.529972
modified: 2026-02-11T15:29:10.529976
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Idaho State University MS in Geosciences thesis
type: Distribution
issued: 2021-09-16T17:45:05.058191
modified: 2026-02-11T15:29:10.522444
access URL: https://etd.iri.isu.edu/ViewSpecimen.aspx

North DakotaView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of North DakotaView.
issued: 2026-02-11T15:29:11.367733
modified: 2026-02-11T15:29:11.367737
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

North DakotaView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T03:10:59.944541
modified: 2026-02-11T15:29:11.359216
access URL: https://ckan.americaview.org/data/factsheets/North_DakotaView_2020_Factsheet.pdf

North DakotaView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-28T19:39:59.769437
modified: 2026-02-11T15:29:11.359618
access URL: https://ckan.americaview.org/data/factsheets/GY21_NorthDakota_Factsheet.pdf

North DakotaView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-06T20:12:07.724859
modified: 2026-02-11T15:29:11.360377
access URL: https://ckan.americaview.org/data/factsheets/GY23_ND_Factsheet.pdf

North DakotaView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:29:16.052734
modified: 2026-02-11T15:29:11.358377
access URL: https://ckan.americaview.org/data/factsheets/North_DakotaView_2018_Factsheet.pdf

North DakotaView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T17:44:18.638462
modified: 2026-02-11T15:29:11.360001
access URL: https://ckan.americaview.org/data/factsheets/GY22_ND_Factsheet.pdf

North DakotaView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:29:43.467328
modified: 2026-02-11T15:29:11.358811
access URL: https://ckan.americaview.org/data/factsheets/North_DakotaView_2019_Factsheet.pdf

North DakotaView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:28:11.742429
modified: 2026-02-11T15:29:11.357530
access URL: https://ckan.americaview.org/data/factsheets/North_DakotaView_2014_Factsheet.pdf

North DakotaView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:28:46.530624
modified: 2026-02-11T15:29:11.357939
access URL: https://ckan.americaview.org/data/factsheets/North_DakotaView_2015_Factsheet.pdf

More than Just a Pretty Picture: How Landsat Images are Made
type: Dataset
description: This educational poster was created as part of the 2017 IowaView Earth as Art exhibit to give exhibit visitors information about how the images were made using Landsat imagery. The poster briefly explains how we see light and how then explains how the Landsat satellite can collect a much broader range of energy.
issued: 2026-02-11T15:29:12.638603
modified: 2026-02-11T15:29:12.638607
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

More than Just a Pretty Picture: How Landsat Images are Made
type: Distribution
format: JPEG
issued: 2021-11-03T02:28:41.492366
modified: 2026-02-11T15:29:12.630533

MichiganView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of MichiganView.
issued: 2026-02-11T15:29:13.535951
modified: 2026-02-11T15:29:13.535955
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

MichiganView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-28T19:27:50.833437
modified: 2026-02-11T15:29:13.528036
access URL: https://ckan.americaview.org/data/factsheets/GY21_Michigan_Factsheet.pdf

MichiganView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-06T16:22:03.834092
modified: 2026-02-11T15:29:13.528796
access URL: https://ckan.americaview.org/data/factsheets/GY23_MI_Factsheet.pdf

MichiganView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:36:39.903836
modified: 2026-02-11T15:29:13.526530
access URL: https://ckan.americaview.org/data/factsheets/MichiganView_2015_Factsheet.pdf

MichiganView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T02:51:18.693941
modified: 2026-02-11T15:29:13.527658
access URL: https://ckan.americaview.org/data/factsheets/MichiganView_2020_Factsheet.pdf

MichiganView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:37:19.618249
modified: 2026-02-11T15:29:13.526914
access URL: https://ckan.americaview.org/data/factsheets/MichiganView_2018_Factsheet.pdf

MichiganView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:35:15.528313
modified: 2026-02-11T15:29:13.526129
access URL: https://ckan.americaview.org/data/factsheets/MichiganView_2014_Factsheet.pdf

MichiganView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T16:44:43.093883
modified: 2026-02-11T15:29:13.528420
access URL: https://ckan.americaview.org/data/factsheets/GY22_MI_Factsheet.pdf

MichiganView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:37:50.487306
modified: 2026-02-11T15:29:13.527289
access URL: https://ckan.americaview.org/data/factsheets/MichiganView_2019_Factsheet.pdf

Watching over Texas from Space Lesson Book
type: Dataset
description: An Earth Observations education resource for grades 4 -12 in support of TEKS covering Earth Science and Environmental Science Standards Watching over Texas from Space has been growing over the past six years, as TexasView has expanded its outreach effort through teacher-training workshops. It is modeled after an online publication from the Canada Centre from Remote Sensing “Watching Over Our Planet from Space” The initial lessons were developed specifically to address the Eighth-grade requirement for Texas’ Earth and Space Science course that states “the student is expected to interpret topographic maps and satellite views to identify land and erosional features and predict how these features may be reshaped by weathering”. Teachers from other grade levels always attend these workshops and found the lessons applicable for diverse requirements. Lessons that provide imagery for teachers to use at multiple grade levels have been integrated with lectures and games, to enable teachers to easily find and use imagery in the classroom and in laboratory activities to address knowledge and skills defined within the Texas Essential Knowledge and Skills (TEKS) standards. The imagery used in the lessons is predominantly over the State of Texas, but the topics that are covered also fit very well with national-level standards defined in the Next Generation Science Standards (NGSS).
issued: 2026-02-11T15:29:14.523080
modified: 2026-02-11T15:29:14.523084
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

2. Student Introduction
type: Distribution
format: PDF
issued: 2022-06-14T14:03:00.121684
modified: 2026-02-11T15:29:14.506725
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/2._Introduction_for_Students.pdf

3.4.2 How is Texas Changing? Scramble Game
type: Distribution
format: PDF
issued: 2022-06-14T17:23:49.402331
modified: 2026-02-11T15:29:14.511247
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.4.2_How_is_Texas_Changing_Scramble_Matching_Game_Fnl.pdf

3.4 How is Texas Changing? Imagery Key
type: Distribution
format: PDF
issued: 2022-06-14T17:20:10.436871
modified: 2026-02-11T15:29:14.510422
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.4_How_is_Texas_Changing_Imagery_Key_TPWD_NASA_EO_USGS_ES.pdf

5.2.1 AmericaView Resource Links
type: Distribution
format: PDF
issued: 2022-06-14T17:40:34.094876
modified: 2026-02-11T15:29:14.513605
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/5.2.1_AmericaView_Resource_Links_final.pdf

Table of Contents
type: Distribution
format: PDF
issued: 2022-06-14T17:44:18.089395
modified: 2026-02-11T15:29:14.514448
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/Table_of_contents_Watching_over_Texas_from_Space_final.pdf

3.4.1 How is Texas Changing? Handout Lesson
type: Distribution
format: PDF
issued: 2022-06-14T17:22:34.227459
modified: 2026-02-11T15:29:14.510825
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.4.1_How_is_Texas_Changing_TPWD_NASA_EO_page_size_F.pdf

3.1.3 Which is Which? I Have, Who Has Game
type: Distribution
format: PDF
issued: 2022-06-14T15:37:45.674602
modified: 2026-02-11T15:29:14.508373
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.1.3_Which_is_Which_I_have_Who_has_TPWD_final.pdf

3.5 Monahans Sandhills Teacher's Guide
type: Distribution
format: PDF
issued: 2022-06-14T17:34:41.466766
modified: 2026-02-11T15:29:14.512431
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.5_Monahans_Sandhills_Change_Over_Time_final.pdf

3.1 Which is Which? Imagery Key
type: Distribution
format: PDF
issued: 2022-06-14T15:25:20.391658
modified: 2026-02-11T15:29:14.507142
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.1_Which_is_Which_imagery_key_TPWD_NASA_EO.pdf

3.1.5 Which is Which? Different Perspectives Matching Quiz
type: Distribution
format: PDF
issued: 2022-06-14T16:54:37.634415
modified: 2026-02-11T15:29:14.509200
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.1.5_Which_is_Which_Diff_Persptves_Matching_quiz_fin.pdf

3.3 Measure It! Mustang Island
type: Distribution
format: PDF
issued: 2022-06-14T17:04:30.842812
modified: 2026-02-11T15:29:14.510000
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.3_Mustang_Island_measure_it_final.pdf

4. Texas as Art
type: Distribution
format: PDF
issued: 2022-06-14T17:36:31.953670
modified: 2026-02-11T15:29:14.512816
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/4._Texas_as_Art_final.pdf

3.1.1 Which is Which? Handout Lessons
type: Distribution
format: PDF
issued: 2022-06-14T15:30:25.897532
modified: 2026-02-11T15:29:14.507547
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.1.1_Which_is_Which_TPWD_NASA_EO_page_size.pdf

3.4.3 How is Texas Changing? Change Pair Quiz
type: Distribution
format: PDF
issued: 2022-06-14T17:25:54.152443
modified: 2026-02-11T15:29:14.511650
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.4.3_Change_Pair_Game_Quiz_Activity_Intro_Fnl.pdf

Zipped Download of all WOTFS Files
type: Distribution
format: ZIP
issued: 2022-06-14T17:45:41.016766
modified: 2026-02-11T15:29:14.514825
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/archive_of_all.zip

3.1.2 Which is Which? Scramble Game
type: Distribution
format: PDF
issued: 2022-06-14T15:32:01.394703
modified: 2026-02-11T15:29:14.507951
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.1.2_Which_is_Which_TPWD_NASA_EO_Scramble_Game_final.pdf

3.2 Find It! San Jacinto
type: Distribution
format: PDF
issued: 2022-06-14T16:56:33.933691
modified: 2026-02-11T15:29:14.509607
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.2_Find_it_San_Jacinto_final.pdf

3.1.4 Which is Which? Poster Activity
type: Distribution
format: PDF
issued: 2022-06-14T16:49:37.255093
modified: 2026-02-11T15:29:14.508788
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.1.4_Which_is_Which_poster_activity_TPWD_NASA_EO_fin.pdf

5.1 Answers Keys to all Activities and Quizzes
type: Distribution
format: PDF
issued: 2022-06-14T17:38:50.041597
modified: 2026-02-11T15:29:14.513202
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/5.1_Answers_to_lesson_games_activities_final.pdf

5.2.2 NASA and USGS Change Imagery Resources
type: Distribution
format: PDF
issued: 2022-06-14T17:42:37.024668
modified: 2026-02-11T15:29:14.514026
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/5.2.2_NASA_and_USGS_change_imagery_resources_final.pdf

3.4.4 How is Texas Changing? Poster Activity
type: Distribution
format: PDF
issued: 2022-06-14T17:29:08.719400
modified: 2026-02-11T15:29:14.512031
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/3.4.4_Poster_Activity_Intro_Final.pdf

1. Teacher Introduction
type: Distribution
format: PDF
issued: 2022-06-14T14:00:36.685886
modified: 2026-02-11T15:29:14.506295
access URL: https://ckan.americaview.org/data/TexasView/WOTFS/1._Introduction_for_Teachers_final.pdf

Cropland and ArcGIS Online Exercise: Pixels and Crop Changes
type: Dataset
description: This exercise developed by Dr. Tom Mueller of California University of Pennsylvania addresses two National Geographic Standards: Standard 1: How to use maps and other geographic representations, geospatial technologies, and spatial thinking to understand and communicate information and Standard 14: How human actions modify the physical environment
issued: 2026-02-11T15:29:16.184892
modified: 2026-02-11T15:29:16.184897
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

PennsylvaniaView Website
type: Distribution
format: PDF
issued: 2021-09-17T20:19:45.402568
modified: 2026-02-11T15:29:16.177607
access URL: https://www.paview.psu.edu/cropland.pdf

Afghanistan - Transmission Network
type: Dataset
description: The existing (2015) and planned transmission network for Afghanistan including lines above 110 kV. Digitization based on the Afghan Energy Information Center (AEIC) maps available in March 2015.
issued: 2025-09-18T20:33:22.658526
modified: 2026-02-19T22:30:57.066313
contact point: Organization
  fn: Alexandros Korkovelos
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://aeic.af/map/1

Transmission network (2015)
type: Distribution
format: GeoJSON
issued: 2026-02-09T13:45:09.654952
modified: 2026-02-19T22:30:57.069369
access URL: https://energydata.info/dataset/d642c8a7-c8a8-4fca-8f63-d99a08c056a4/resource/15cff570-4888-49c4-887b-816b29a59a4a/download/afgexistingtransmissionwgs84.geojson

Transmission network (2015)
type: Distribution
format: ZIP
issued: 2026-02-09T13:45:25.449425
modified: 2026-02-19T22:30:57.069883
access URL: https://energydata.info/dataset/d642c8a7-c8a8-4fca-8f63-d99a08c056a4/resource/3869ea41-fd6e-4b9b-a044-9faec8acdf5d/download/afghanistantransmission2015.zip

Transmission network (planned)
type: Distribution
format: ZIP
issued: 2026-02-09T13:46:05.193002
modified: 2026-02-19T22:30:57.070745
access URL: https://energydata.info/dataset/d642c8a7-c8a8-4fca-8f63-d99a08c056a4/resource/5a790a80-2e39-4f76-a193-7565f72cd8c7/download/afghanistantransmissionplanned.zip

Transmission network (planned)
type: Distribution
format: GeoJSON
issued: 2026-02-09T13:45:46.341229
modified: 2026-02-19T22:30:57.070305
access URL: https://energydata.info/dataset/d642c8a7-c8a8-4fca-8f63-d99a08c056a4/resource/bdce160d-d54a-49c2-bafb-93ea80cbf5cd/download/afgfuturetransmissionwgs84.geojson

Empirical Analysis of ICESat‐2 ATLAS's Bathymetric Mapping Capability
type: Dataset
description: Summary of previous results (some presented at JALBTCX) • NASA MABEL (high- altitude airborne emulator for ATLAS) capable of mapping bathymetry • Agrees with reference bathymetry to within 0.7 m RMS • Visible Infrared Imaging Radiometer Suite (VIIRS) Kd490 useful in assessing utility of ATLAS for bathymetric mapping worldwide Objectives of current (post-launch) research 1. Identify examples of ICESat-2 ATLAS bathymetry 2. Empirically assess bathymetric mapping accuracy through comparison against reference data 3. Assess ATLAS’s maximum depth penetration capability as a function of Kd and Secchi depth through analysis at multiple locations around the globe
issued: 2026-02-11T15:29:16.944660
modified: 2026-02-11T15:29:16.944665
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Coastal Mapping and Charting Workshop Power Point Presentation
type: Distribution
format: PDF
issued: 2021-09-17T19:58:29.063258
modified: 2026-02-11T15:29:16.936887
access URL: https://ckan.americaview.org/data/OregonView/Parrish_JALBTCX_2019.pdf

Assessing topographic controls on vegetation characteristics in Chittagong Hill Tracts (CHT) from remotely sensed data
type: Dataset
description: The study aimed at assessing multi-scale correlation between vegetation characteristics and topographic parameters in Chittagong Hill Tracts (CHT), Bangladesh. Traditional shifting cultivation and its associated activities are considered unsuitable practices in CHT, therefore, the alternative land use systems e.g. agroforestry, tree farming, and afforestation are more suitable and sustainable in the region. We assessed the topographic controls on vegetation indices (NDII, NDVI, and EVI and their corresponding characteristics) to identify the potential sites for alternative land use practices. The result shows that NDII, EVI, and NDVI values vary 48%, 27%, and 23%, respectively, relative to their respective mean values. The soil moisture decreases with higher slope and elevation having the coefficient (R2) values of − 0.17 and − 0.19, respectively because the underlying soil cannot retain rainwater as run-off are comparatively faster in higher elevation and slope. Elevation and slope exert influence on all vegetation characteristics under consideration. Soil moisture is negatively paired with NDVI (R2 value of − 0.04) indicating, interestingly, that soil moisture content does not influence vegetation water content. Aspect has no significant correlation to any of the vegetation indices (R2 is ~ 0.02 in all cases) and, hence, while selecting sites for alternative agricultural practices, the topographic parameter of aspect can be ignored. NDII and EVI are significantly correlated more with elevation than slope. Thus, the potential site for the biomass or vegetation which retains moisture content should be in an area that is more susceptible to elevation followed by slope. In contrast, NDVI is significantly correlated with the soil moisture, slope, and altitude. Therefore, to get a generalized vigor of vegetation of higher green leaf content, primary production, chlorophyll content, and green leaf biomass, one must consider a combination of elevation and slope, as well a…
issued: 2026-02-11T15:29:17.741187
modified: 2026-02-11T15:29:17.741192
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Applications: Society and Environment Volume 11
type: Distribution
issued: 2021-09-15T18:55:55.796897
modified: 2026-02-11T15:29:17.733458
access URL: https://www.sciencedirect.com/science/article/abs/pii/S2352938518300211

Accuracy Assessment of Remotely Sensed Data Video Series
type: Dataset
description: Learn everything about why and how to do an accuracy assessment on remotely sensed data through online videos and associated exercises. This video series was developed by Dr. Russ Congalton with New HampshireView.
issued: 2026-02-11T15:29:18.477062
modified: 2026-02-11T15:29:18.477067
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Accuracy Assessment Exercise 3: Stratified Random Sampling
type: Distribution
format: PDF
issued: 2021-04-29T14:47:41.844110
modified: 2026-02-11T15:29:18.469791
access URL: https://ckan.americaview.org/data/New_HampshireView/Accuracy_Assessment_EX3_StratRandomSampling.pdf

Accuracy Assessment Exercise 1: Descriptive Statistics
type: Distribution
format: PDF
issued: 2021-04-29T14:45:55.440474
modified: 2026-02-11T15:29:18.469023
access URL: https://ckan.americaview.org/data/New_HampshireView/Accuracy_Assessment_EX1_DescriptiveStats.pdf

Accuracy Assessment Exercise 2: Random Sampling
type: Distribution
format: PDF
issued: 2021-04-29T14:46:47.670064
modified: 2026-02-11T15:29:18.469412
access URL: https://ckan.americaview.org/data/New_HampshireView/Accuracy_Assessment_EX2_RandomSampling.pdf

Video Series
type: Distribution
issued: 2021-04-29T14:43:01.277271
modified: 2026-02-11T15:29:18.468611
access URL: https://www.youtube.com/playlist

MontanaView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of MontanaView.
issued: 2026-02-11T15:29:19.278055
modified: 2026-02-11T15:29:19.278060
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

MontanaView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:05:04.034008
modified: 2026-02-11T15:29:19.267729
access URL: https://ckan.americaview.org/data/factsheets/MontanaView_2015_Factsheet.pdf

MontanaView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T03:01:57.177036
modified: 2026-02-11T15:29:19.268953
access URL: https://ckan.americaview.org/data/factsheets/MontanaView_2020_Factsheet.pdf

MontanaView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-28T19:33:56.492838
modified: 2026-02-11T15:29:19.269362
access URL: https://ckan.americaview.org/data/factsheets/GY21_Montana_Factsheet.pdf

MontanaView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T16:51:27.503011
modified: 2026-02-11T15:29:19.269757
access URL: https://ckan.americaview.org/data/factsheets/GY22_MT_Factsheet.pdf

MontanaView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-06T19:57:03.423894
modified: 2026-02-11T15:29:19.270137
access URL: https://ckan.americaview.org/data/factsheets/GY23_MT_Factsheet.pdf

MontanaView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:05:33.483266
modified: 2026-02-11T15:29:19.268126
access URL: https://ckan.americaview.org/data/factsheets/MontanaView_2018_FactSheet.pdf

MontanaView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:06:01.283013
modified: 2026-02-11T15:29:19.268514
access URL: https://ckan.americaview.org/data/factsheets/MontanaView_2019_Factsheet.pdf

MontanaView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:04:22.251592
modified: 2026-02-11T15:29:19.267305
access URL: https://ckan.americaview.org/data/factsheets/MontanaView_2014_Factsheet.pdf

Automated Fractional Snow Cover Monitoring From Near-Surface Remote Sensing In Grassland
type: Dataset
description: Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. I developed a mostly semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research, which make use of RGB images only, the use of the monochrome RGB + NIR (near-infrared) channel reduced pixel misclassification and increased accuracy. The results have an average RMSE of 7.67 compared to visual estimates. This is a promising outcome, although not every PhenoCam system has NIR capability.
issued: 2026-02-11T15:29:20.341270
modified: 2026-02-11T15:29:20.341275
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

MS Thesis, University of North Dakota
type: Distribution
issued: 2021-09-16T19:01:34.296207
modified: 2026-02-11T15:29:20.333765
access URL: https://commons.und.edu/theses/3260/

Geospatial Deep Learning Seminar Online Course
type: Dataset
description: This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: 1. explain how ANNs work including weights, bias, activation, and optimization. 2. describe and explain different loss and assessment metrics and determine appropriate use cases. 3. use the tensor data model to rep…
issued: 2026-02-11T15:29:21.093995
modified: 2026-02-11T15:29:21.093999
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Course Webpage
type: Distribution
format: HTML
issued: 2022-09-10T15:55:45.834129
modified: 2026-02-11T15:29:21.086591
access URL: https://wvview.org/geospatdl.html

Google Earth Web Tours - A History of the Landsat Program
type: Dataset
description: UtahView developed an interactive tour about the history of the Landsat Program using Google Earth Web.
issued: 2026-02-11T15:29:21.876735
modified: 2026-02-11T15:29:21.876740
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

GY19 UtahView MiniGrant - Google Earth Web Tours - A History of the Landsat Program
type: Distribution
format: HTML
issued: 2021-11-04T01:19:51.375926
modified: 2026-02-11T15:29:21.869321
access URL: https://bit.ly/UtahView_HistoryLandsat

The Pioneers of the Landsat Program Poster
type: Dataset
description: Learn about 12 individuals that contributed to the development of the Landsat Program.
issued: 2026-02-11T15:29:22.692488
modified: 2026-02-11T15:29:22.692492
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Poster Download
type: Distribution
format: PDF
issued: 2023-03-23T18:36:16.072897
modified: 2026-02-11T15:29:22.684433
access URL: https://ckan.americaview.org/data/UtahView/Pioneers_of_Landsat_36x36_Update20230815.pdf

Iowa BMP Mapping Project Website
type: Dataset
description: The goal of the Iowa BMP (Best Management Practices) Mapping Project is to provide a complete baseline set of BMPs dating from the 2007-2010 timeframe for use in watershed modeling, historic occurrence, and future practice tracking. The BMPs mapped are: Terraces, Water and Sediment Control Basins (WASCOB), Grassed Waterways, Pond Dams, Contour Strip Cropping and Contour Buffer Strips. We can't guarantee that mapped practices meet NRCS standards or that they are actually the indicated practice since no ground reference analysis is being performed. Data being utilized to digitize the BMPs include LiDAR derived products such as DEM, Hillshade and Slope grids; CIR aerial photography from the 2007-2010 timeframe, NAIP aerial photography and historic aerial photography.
issued: 2026-02-11T15:29:23.493591
modified: 2026-02-11T15:29:23.493597
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Iowa BMP Mapping Project Website
type: Distribution
issued: 2021-11-03T02:14:41.552837
modified: 2026-02-11T15:29:23.486057
access URL: https://www.gis.iastate.edu/gisf/projects/conservation-practices

AmericaView Form 990
type: Dataset
description: Form 990 Return of Organization Exempt From Income Tax of AmericaView provided to the IRS.
issued: 2026-02-11T15:29:24.270378
modified: 2026-02-11T15:29:24.270382
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

July 2021 to June 2022
type: Distribution
format: PDF
issued: 2023-07-20T14:50:09.531239
modified: 2026-02-11T15:29:24.261697
access URL: https://ckan.americaview.org/data/AmericaView/990_year_End_June_30_2022.pdf

July 2023 to June 2024
type: Distribution
format: PDF
issued: 2024-11-07T19:45:42.155835
modified: 2026-02-11T15:29:24.262470
access URL: https://ckan.americaview.org/data/AmericaView/AmericaView_990_Year_End_June302024.pdf

July 2019 to June 2020
type: Distribution
format: PDF
issued: 2022-04-25T20:17:56.140390
modified: 2026-02-11T15:29:24.260874
access URL: https://ckan.americaview.org/data/AmericaView/AmericaView_990_year_End_June_30_2020_final.pdf

July 2018 to June 2019
type: Distribution
format: PDF
issued: 2022-04-25T20:17:21.690678
modified: 2026-02-11T15:29:24.260400
access URL: https://ckan.americaview.org/data/AmericaView/AmericaView_990_year_End_June_30_2019_final.pdf

July 2020 to June 2021
type: Distribution
format: PDF
issued: 2023-07-20T14:49:31.438297
modified: 2026-02-11T15:29:24.261290
access URL: https://ckan.americaview.org/data/AmericaView/990_year_End_June_30_2021.pdf

July 2022 to June 2023
type: Distribution
format: PDF
issued: 2024-02-02T16:21:27.632212
modified: 2026-02-11T15:29:24.262088
access URL: https://ckan.americaview.org/data/AmericaView/AmericaView_990_year_End_June_30_2023_final.pdf

July 2024 to June 2025
type: Distribution
format: PDF
issued: 2025-11-20T13:54:44.175035
modified: 2026-02-11T15:29:24.262842
access URL: https://ckan.americaview.org/data/AmericaView/2025_990_Tax_Returns.pdf

Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics
type: Dataset
description: Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.
issued: 2026-02-11T15:29:25.266860
modified: 2026-02-11T15:29:25.266865
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Volume 12 Issue 12
type: Distribution
format: HTML
issued: 2021-09-19T22:50:36.917740
modified: 2026-02-11T15:29:25.258639
access URL: https://www.mdpi.com/2072-4292/12/12/1905

Water surface temperature map for Iron Gate Reservoir
type: Dataset
description: In Summer 2016, an OregonView-sponsored research team, led by OSU graduate student, Kory Kellum, tested procedures for using Landsat 8 (L8) TIRS bands and in situ data for generating multi-temporal water surface temperature data products for the Iron Gate Reservoir, impounded by the Iron Gate Dam on the Klamath River. The water surface temperature map for Iron Gate Reservoir on July 20, 2016 is provided as a download from the OregonView website.
issued: 2026-02-11T15:29:26.024017
modified: 2026-02-11T15:29:26.024021
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Water surface temperature map for Iron Gate Reservoir
type: Distribution
issued: 2021-11-03T02:39:31.582581
modified: 2026-02-11T15:29:26.016510
access URL: https://ckan.americaview.org/data/OregonView/Klamath_Water_Surf_Temp.zip

KU Students Wrap Up Course in Aerial Mapping Using Drones
type: Dataset
description: LAWRENCE — Fall break is barely behind us, but a group of University of Kansas students has just finished an innovative eight-week course in using drones to develop aerial maps. Over the past two months, they’ve visited sites in KU's West District and at the Baker Wetlands, taking still images and videos over those areas. “The drone mapping course has been excellent in providing a hands-on experience with the drones,” said Siddharth Shankar, graduate student from Lucknow, India. “The course has focused not just on drones and how to fly them but also has made us aware of the FAA rules and regulations about drone flying and safety precautions. “My research has been in glaciology, with the study of icebergs in Greenland. The drone mapping course has provided new insights into incorporating it with my research in the near future.” The course, offered annually during the fall semester, is designed to teach students about the rapidly growing technology of small unmanned aerial systems, referred to as drones, and its wide-ranging applications — which include search-and-rescue, real estate and environmental monitoring. Students in the course come from a variety of disciplines including geography & atmospheric science, geology, ecology & evolutionary biology and civil engineering. Enthusiasm for the course has been very high, and it has filled rapidly each time it has been offered.
issued: 2026-02-11T15:29:26.792903
modified: 2026-02-11T15:29:26.792908
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

KU News
type: Distribution
issued: 2021-09-16T17:57:44.065370
modified: 2026-02-11T15:29:26.785073
access URL: https://news.ku.edu/ku-students-wrap-new-course-aerial-mapping-using-drones

Nigeria - Katsina State Settlement Dataset
type: Dataset
description: Data on Nigeria Settlement Location and Distribution downloaded from the The Bill & Melinda Gates Foundation Vaccination Tracking System (VTS). To learn more about the VTS project, please visit http://vts.eocng.org/Home/About?campaignId=0
issued: 2025-09-18T20:33:30.718929
modified: 2026-02-19T22:30:58.365086
contact point: Organization
  fn: Shaky Sherpa
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://vts.eocng.org/

Katsina State Settlement
type: Distribution
format: GeoJSON
issued: 2026-02-09T10:59:19.542062
modified: 2026-02-19T22:30:58.368806
access URL: https://energydata.info/dataset/e076dd4d-e209-4da7-94b8-1f398bd3d362/resource/3609b474-a89b-4834-a90b-5cd6c490a2fe/download/katsinastategatessettlement.geojson

Katsina State Settlement
type: Distribution
format: CSV
issued: 2026-02-09T10:58:46.399440
modified: 2026-02-19T22:30:58.368326
access URL: https://energydata.info/dataset/e076dd4d-e209-4da7-94b8-1f398bd3d362/resource/c1a88761-c52d-4585-a567-f3d68ca3c877/download/katsinastategatessettlement.csv

Remote Sensing of Wildfire Online Course
type: Dataset
description: Participants in this course will learn about remote sensing of wildfires from instructors at the University of Alaska Fairbanks, located in one of the world’s most active wildfire zones. Students will learn about wildfire behavior, and get hands-on experience with tools and resources used by professionals to create geospatial maps that support firefighters on the ground. Upon completion, students will be able to: Access web resources that provide near real-time updates on active wildfires, Navigate databases of remote sensing imagery and data, Analyze geospatial data to detect fire hot spots, map burn areas, and assess severity, Process image and GIS data in open source tools like QGIS and Google Earth Engine.
issued: 2026-02-11T15:29:27.621538
modified: 2026-02-11T15:29:27.621542
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Course Registration
type: Distribution
issued: 2021-05-04T14:17:43.633836
modified: 2026-02-11T15:29:27.614153
access URL: https://www.edx.org/course/remote-sensing-of-wildfires

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers
type: Dataset
description: Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.
issued: 2026-02-11T15:29:28.448701
modified: 2026-02-11T15:29:28.448706
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Volume 11 Issue 11
type: Distribution
issued: 2021-09-17T18:57:09.732016
modified: 2026-02-11T15:29:28.440924
access URL: https://www.mdpi.com/2072-4292/11/11/1380

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery
type: Dataset
description: Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha−1. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R2 of 0.26 and RMSE of 70.1 Mg ha−1. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R2 of 0.40 and an RMSE of 108.2 Mg ha−1 compared to the full lidar coverage model with an R2 of 0.58 and an RMSE of 96.0 Mg ha−1. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collectio…
issued: 2026-02-11T15:29:29.250964
modified: 2026-02-11T15:29:29.250968
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Volume 11 Issue 16
type: Distribution
format: HTML
issued: 2021-09-16T19:05:16.903733
modified: 2026-02-11T15:29:29.242340
access URL: https://www.mdpi.com/2072-4292/11/16/1906

Liberia - Multi-Tier Framework (MTF) Survey
type: Dataset
description: The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (which includes raw data sets of dta format), excel spreadsheet, pdf, and docx.
issued: 2025-09-18T20:33:34.066342
modified: 2026-02-19T22:30:59.126764
contact point: Organization
  fn: Alisha Pinto
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Raw Data.zip
type: Distribution
format: ZIP
issued: 2026-02-09T12:07:12.164800
modified: 2026-02-19T22:30:59.130753
access URL: https://energydata.info/dataset/e57ef195-4fff-4a96-95c7-69677b2aa9b8/resource/09da19f0-be90-485a-94d2-0191dc986647/download/raw-data-.zip

MTF_LIBERIA_Questionnaire_Household
type: Distribution
format: PDF
issued: 2026-02-09T12:09:04.932028
modified: 2026-02-19T22:30:59.131534
access URL: https://energydata.info/dataset/e57ef195-4fff-4a96-95c7-69677b2aa9b8/resource/20d9e06b-6247-48ce-9767-29b79e429a4e/download/mtf_questionnaire_liberia.pdf

Liberia_Weights.dta
type: Distribution
format: Other
issued: 2026-02-09T12:09:26.659639
modified: 2026-02-19T22:30:59.131919
access URL: https://energydata.info/dataset/e57ef195-4fff-4a96-95c7-69677b2aa9b8/resource/4e40d9d7-cca9-467f-a868-c08da28142ca/download/liberia_weights.dta

Codebook_Liberia.xlsx
type: Distribution
format: XLSX
issued: 2026-02-09T12:08:43.884155
modified: 2026-02-19T22:30:59.131146
access URL: https://energydata.info/dataset/e57ef195-4fff-4a96-95c7-69677b2aa9b8/resource/86cb2e46-1943-4349-8731-85937a732615/download/codebook_liberia.xlsx

MTF Energy Access Country Diagnostics Report_LIBERIA
type: Distribution
format: PDF
issued: 2026-02-09T12:05:51.293944
modified: 2026-02-19T22:30:59.129886
access URL: https://energydata.info/dataset/e57ef195-4fff-4a96-95c7-69677b2aa9b8/resource/8d5ce1cc-a18e-4da0-857e-d2530487d973/download/mtf-energy-access-country-diagnostics-report_liberia.pdf

MTF_Sampling_Strategy _LIBERIA.docx
type: Distribution
format: DOC
issued: 2026-02-09T12:06:55.885172
modified: 2026-02-19T22:30:59.130345
access URL: https://energydata.info/dataset/e57ef195-4fff-4a96-95c7-69677b2aa9b8/resource/d3ce5acf-e686-4b14-97f7-a92e9d606047/download/mtf_sampling_strategy_liberia.docx

Active-Passive Spaceborne Data Fusion for Mapping Nearshore Bathymetry
type: Dataset
description: In anticipation of the National Aeronautics and Space Administration (NASA) ICESat-2 mission, which will employ the Advanced Topographic Laser Altimeter System (ATLAS), a 532 nm photon-counting Light Detection and Ranging (lidar), we demonstrate a spaceborne data-fusion approach that has the potential to significantly shrink the global nearshore data gap often referred to as the "white ribbon". Bathymetry algorithms relying on multispectral imagery are conventionally limited by the availability of in situ reference depths, particularly in remote or difficult-to-map areas. Therefore, a completely spaceborne approach could greatly extend the usefulness of such algorithms. The approach is tested with data from NASA's airborne ICESat-2 ATLAS simulator, Multiple Altimeter Beam Experimental Lidar (MABEL), and passive optical imagery from Landsat-8 using an existing spectral-ratio algorithm. The output bathymetric data set agrees with high-resolution Fugro LADS MK II bathymetric data to within an RMS difference of 1.1 m. The spatiotemporal variability of areas where this spaceborne data-fusion approach will potentially be useful is evaluated, based on worldwide coastal water clarity as interpreted from Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data.
issued: 2026-02-11T15:29:30.003259
modified: 2026-02-11T15:29:30.003263
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Photogrammetric Engineering & Remote Sensing, Volume 85, Number 4
type: Distribution
format: HTML
issued: 2021-09-17T19:49:05.698708
modified: 2026-02-11T15:29:29.996022
access URL: https://www.ingentaconnect.com/contentone/asprs/pers/2019/00000085/00000004/art00013

Iowa BMP Mapping Project Data Download Website
type: Dataset
description: This web app allows users to search a map of Iowa for conservation practice data by HUC 12 watershed and then download it as a pdf or geodatabase.
issued: 2026-02-11T15:29:30.732424
modified: 2026-02-11T15:29:30.732429
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Iowa BMP Mapping Project Data Download Website
type: Distribution
format: HTML
issued: 2021-11-03T02:15:40.178990
modified: 2026-02-11T15:29:30.724525
access URL: https://bensonvip.gis.iastate.edu/IA_BMPs/

Mapping Invasive Phragmites australis in the Old Woman Creek Using Remote Sensing
type: Dataset
description: The application of remote sensing techniques in mapping, classifying and monitoring land cover, land use and vegetation are popular among the researchers and scientists for several decades. It became more productive and economical in recent years with the advancement of information technology in a sophisticated and revolutionary manner. Currently, remote sensing is a widely used effective technique that provides spatial and temporal information about vegetation and invasive species in wetlands. The first objective of this study was to assess the effectiveness of the data obtained via Unmanned Ariel Vehicles (UAV) to identify invasive Phragmites australis in the Old Woman Creek (OWC) in Ohio. Secondly, the study aimed to determine the most suitable algorithm to distinguish between Phragmites australis and other vegetation types using pixel based and object based classification methods and a combination of feature layers derived from the UAV images. Pixel based classification found to be performing better than object based classification. Pixel based Neural Network (NN) was identified as the best classifier to map Phragmites in OWC with the least error of omission of 1.59% and the overall accuracy of 94.80% based on the Sequoia image acquired in August that was stacked with Canopy Height Model (CHM) from August and NDVI, which was derived using UAV data acquired in October (NDVIOct). The study emphasizes the necessity of a suitable sampling method and the use of optimum parameters of non-parametric classifiers. The study provides future directions for data acquisition to map Phrgamites at early and mid-summer to find data to eradicate Phragmites effectively in OWC estuary.
issued: 2026-02-11T15:29:31.693945
modified: 2026-02-11T15:29:31.693950
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

OhioLink / Graduate College of Bowling Green State University thesis
type: Distribution
format: PDF
issued: 2021-09-17T19:13:08.213156
modified: 2026-02-11T15:29:31.685727
access URL: https://etd.ohiolink.edu/apexprod/rws_etd/send_file/send

WyomingView Intern Videos
type: Dataset
description: WyomingView interns (University of Wyoming students) describe the value of remotely sensed data for mapping and monitoring crop and rangelands.
issued: 2026-02-11T15:29:32.370452
modified: 2026-02-11T15:29:32.370456
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Website
type: Distribution
format: HTML
issued: 2023-07-24T15:07:21.142838
modified: 2026-02-11T15:29:32.362496
access URL: http://wyomingview.blogspot.com/p/multimedia.html

Nepal - Solar Radiation Measurement Data
type: Dataset
description: Data repository for measurements from 5 automated solar stations in Nepal. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values for solar radiation levels, temperature, air pressure, liquid precipitation and wind speed. For more information and additional outputs, please visit: http://esmap.org/re_mapping_nepal For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Nepal-Solar Radiation Measurement Data, 2018,
issued: 2025-09-18T20:33:37.825184
modified: 2026-02-19T22:30:59.996826
contact point: Organization
  fn: Roman Affolter
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

Solar-Measurements_Nepal_Dharan_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:33:48.548725
modified: 2026-02-19T22:31:00.005110
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/093c7cc7-3cb1-4678-9449-aae8561b9416/download/solar-measurements_nepal_dharan_wb-esmap_header.xlsx

olar-Measurements_Nepal_Lumle_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:36:14.895964
modified: 2026-02-19T22:31:00.007159
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/24e4aa8b-fe5c-40db-b9e4-6872e9c07022/download/solar-measurements_nepal_lumle_wb-esmap_qc.csv

Nepal Installation Report WBG-ESMAP.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:26:17.219551
modified: 2026-02-19T22:31:00.000677
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/27f353a4-da9c-4834-a837-97f385f3d532/download/csps-esmap_nepal_installationreport_wbg-esmap.pdf

SRMI-Presentation-Nepal_ESMAP-WBG.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:29:31.800745
modified: 2026-02-19T22:31:00.003158
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/3614f0c3-6932-47bf-8627-7a15da9f2090/download/srmi-presentation-nepal_esmap-wbg.pdf

Nepal Implimentation Plan ESMAP-WBG.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:25:55.337266
modified: 2026-02-19T22:31:00.000226
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/406baf9d-a932-4dc2-b932-9d146b7320d8/download/csps-esmap_nepal_implimentation-plan_esmap-wbg.pdf

Solar-Measurements_Nepal_Kathmandu_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:35:09.798041
modified: 2026-02-19T22:31:00.006334
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/4fc9113b-8495-4f2d-bc25-aaa4664e0878/download/solar-measurements_nepal_kathmandu_wb-esmap_qc.csv

Solargis_Nepal_Solar-Resource-Webinar_2021-04-27_WB-ESMAP.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:29:11.701310
modified: 2026-02-19T22:31:00.002756
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/5516d4b4-ca42-4ef5-9e70-73d56dc33c95/download/solargis_nepal_solar-resource-webinar_2021-04-27_wbg-esmap.pdf

Solar-Measurements_Nepal_Nepalgunj_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:37:10.296239
modified: 2026-02-19T22:31:00.007928
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/6710d074-df02-4492-8f4f-590578d1512b/download/solar-measurements_nepal_nepalgunj_wb-esmap_qc.csv

Nepal Presentation Final Results, Solar Measurement Campaign_April-26-WB-ESMAP.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:30:40.173474
modified: 2026-02-19T22:31:00.003541
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/7a19b634-a489-415f-930a-7ec284b62963/download/nepal_presentation-final_results_solar-measurement-campaign_april-26_wbg_esm…

Nepal_Presentation-Follow-Up-Activities_Solar-Measurement-Campaign_April-27_WBG_ESMAP.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:31:50.266678
modified: 2026-02-19T22:31:00.003951
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/7e790f1b-2d32-4bd1-9769-749f8489118d/download/nepal_presentation-follow-up-activities_solar-measurement-campaign_april-27_…

Solar-Measurements_Nepal_Nepalgunj_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:38:08.073924
modified: 2026-02-19T22:31:00.008329
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/876e626b-f8be-44c3-b21e-06eb7c09e90b/download/solar-measurements_nepal_nepalgunj_wb-esmap_header.xlsx

Solargis_Solar-Model-Adaptation_Nepal_ESMAP-WBG.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:28:19.241341
modified: 2026-02-19T22:31:00.002350
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/98615a66-a122-4875-8b86-5875ede4d51b/download/solargis_solar-model-adaptation_nepal_esmap-wbg.pdf

Solar-Measurements_Nepal_Jumla_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:34:03.081026
modified: 2026-02-19T22:31:00.005551
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/9c30625c-da0a-4515-b4da-1f04f4f49af8/download/solar-measurements_nepal_jumla_wb-esmap_qc.csv

Solar Energy Atlas of Nepal
type: Distribution
format: PDF
issued: 2026-02-09T13:27:26.082323
modified: 2026-02-19T22:31:00.001920
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/9f071c2e-c7e8-4239-8ebd-f4c5f429d0ec/download/solarenergyatlas_nepal_esmap-wbg.pdf

Nepal 12-MonthReport ESMAP-WBG.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:26:43.280309
modified: 2026-02-19T22:31:00.001107
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/c3b71dc1-84b3-4f20-ab5d-bf267cbdb4c7/download/csps-esmap_nepal_12monthreport_esmap-wbg.pdf

Program - Solar Resource Measurement in Nepal - April 26-27, 2021.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:32:49.247814
modified: 2026-02-19T22:31:00.004331
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/d43f0ae4-6b9e-45f2-895f-13dad1f1092d/download/program-solar-resource-measurement-in-nepal-april-26-27-2021.pdf

Solar-Measurements_Nepal_Kathmandu_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:35:59.182305
modified: 2026-02-19T22:31:00.006766
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/db7faedd-10d9-4916-ac73-fc000cb84d1b/download/solar-measurements_nepal_kathmandu_wb-esmap_header.xlsx

Solar-Measurements_Nepal_Jumla_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:34:55.752507
modified: 2026-02-19T22:31:00.005950
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/de5c4222-248b-4605-8445-730470178993/download/solar-measurements_nepal_jumla_wb-esmap_header.xlsx

Nepal 24-Month Report ESMAP-WBG.pdf
type: Distribution
format: PDF
issued: 2026-02-09T13:27:00.653483
modified: 2026-02-19T22:31:00.001518
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/e31754c1-93dc-4e82-8358-4b2d86edf3ca/download/csps-esmap_nepal_24monthreport_esmap-wbg.pdf

Solar-Measurements_Nepal_Lumle_WB-ESMAP_Header
type: Distribution
format: XLSX
issued: 2026-02-09T13:36:53.381762
modified: 2026-02-19T22:31:00.007525
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/f30b3ee7-8274-4ad3-b1be-05acc6c419eb/download/solar-measurements_nepal_lumle_wb-esmap_header.xlsx

Solar-Measurements_Nepal_Dharan_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T13:33:10.654902
modified: 2026-02-19T22:31:00.004705
access URL: https://energydata.info/dataset/ead8d2fa-de59-48d3-8693-817b36b3a470/resource/f712d801-1f3b-4897-bc7e-61e13ba0a61f/download/solar-measurements_nepal_dharan_wb-esmap_qc.csv

Seasonal home ranges and habitat selection of three elk (Cervus elaphus) herds in North Dakota
type: Dataset
description: Changes in land use have resulted in range shifts of many wildlife species, including those entering novel environments, resulting in the critical need to understand their spatial ecology to inform ecosystem effects and management decisions. Dispersing elk (Cervus elaphus) were colonizing areas of suitable habitat in the Northern Great Plains, USA, resulting in crop depredation complaints in these areas. Although state resource managers had little information on these elk herds, limited evidence suggested temporal movements into Canada. We collected and analyzed essential information on home range and habitat selection for 3 elk herds residing in North Dakota. We captured 5 adult female elk in each study area, affixed global positioning system collars, and monitored them for 1 year (2016–2017). We estimated diel period, seasonal, and hunting season home ranges using Brownian Bridge Movement Models for each individual. We analyzed habitat selection using multinomial logit models to test for differences in use of land classes, and for departures from proportionate use based on random sampling; our predictor variables included individual elk, diel period, and season. Home ranges differed between the 3 herds, seasons, and diel period; gun and winter season home ranges were both larger than in summer, as was night when compared with day. Female elk generally restricted themselves to cover during the day and entered open areas at night and during winter months. Our results also suggest that elk in our study areas tended to seek more cover, and in the case of our Turtle Mountain study area, some cross into Canada during gun season. Our study provides a better understanding of the spatial ecology of elk in the Northern Great Plains while highlighting the need for enhanced international cooperative management efforts.
issued: 2026-02-11T15:29:33.252060
modified: 2026-02-11T15:29:33.252064
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

PLOS One journal
type: Distribution
issued: 2021-09-17T13:44:10.797733
modified: 2026-02-11T15:29:33.244384
access URL: https://doi.org/10.1371/journal.pone.0211650

Mapping Kansas Ecosystems
type: Dataset
description: This StoryMap explains introductory remote sensing concepts and the influence of environmental factors and human land use on the historic and current Kansas landscape. It also takes you on a tour of Kansas, as seen on the ground and from above. It also shows you the new Kansas Ecological Systems Map, showing the 49 mapped vegetation and land cover types.
issued: 2026-02-11T15:29:34.121396
modified: 2026-02-11T15:29:34.121400
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Kansas Ecosystems StoryMap
type: Distribution
issued: 2023-01-24T17:09:14.155289
modified: 2026-02-11T15:29:34.113796
access URL: https://storymaps.arcgis.com/stories/90487a54e30e4362a6ac01006241e2c2

Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance
type: Dataset
description: NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit…
issued: 2026-02-11T15:29:35.073157
modified: 2026-02-11T15:29:35.073162
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Volume 11 Issue 14
type: Distribution
format: HTML
issued: 2021-09-17T19:46:55.833237
modified: 2026-02-11T15:29:35.065502
access URL: https://www.mdpi.com/2072-4292/11/14/1634/htm

An Introduction to Precision Agriculture: An Educator's Guide to Agricultural Earth Observation
type: Dataset
description: Small unmanned aircraft systems (sUAS) are being increasingly utilized to support an array of applications including agriculture, natural resource management, facility/infrastructure inspections, and package delivery. Currently, precision agriculture is the fastest growing market for sUAS. One of the major barriers to the adoption of transformative innovation is education. Currently, educators lack classroom ready resources that can be utilized to introduce precision agriculture. This 120 page publication was developed to directly address this need. This publication will support the needs of agricultural educators across Virginia, by providing an overview of precision agricultural principles, coupled with activities and learning lessons. All of the developed curriculum modules incorporate various STEM related concepts. The information, resources, and activities in this workbook are provided as an 'educational buffet'. These materials do not need to be consumed all at once. Educators can sample, take, modify, and deliver just the topics / activities / resources in this workbook that are needed.
issued: 2026-02-11T15:29:35.865676
modified: 2026-02-11T15:29:35.865681
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Website
type: Distribution
format: HTML
issued: 2023-07-31T15:47:21.187841
modified: 2026-02-11T15:29:35.857074
access URL: https://www.pubs.ext.vt.edu/CNRE/cnre-142/cnre-142.html

2016 Earth Observation Day Poster
type: Dataset
description: Change is everywhere. We see change best when we can compare things—like before and after images. Landsat helps us do this. Landsat satellites have been imaging our planet over four decades, allowing us to see how the Earth’s land surface has changed. This poster provides examples of change over time as observed by Landsat satellites.
issued: 2026-02-11T15:29:36.573478
modified: 2026-02-11T15:29:36.573483
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

2016 EOD Landsat Change over Time Poster
type: Distribution
format: PDF
issued: 2021-06-23T19:40:27.333418
modified: 2026-02-11T15:29:36.565666
access URL: https://science.nasa.gov/mission/landsat/outreach/change-over-time-poster/

CaliforniaView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of CaliforniaView
issued: 2026-02-11T15:29:37.458033
modified: 2026-02-11T15:29:37.458038
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

CaliforniaView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-09T19:18:35.875253
modified: 2026-02-11T15:29:37.447768
access URL: https://ckan.americaview.org/data/factsheets/CaliforniaView_2015_Factsheet.pdf

CaliforniaView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-09T19:21:18.232878
modified: 2026-02-11T15:29:37.448205
access URL: https://ckan.americaview.org/data/factsheets/CaliforniaView_2018_Factsheet.pdf

CaliforniaView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-03T16:10:43.904044
modified: 2026-02-11T15:29:37.450256
access URL: https://ckan.americaview.org/data/factsheets/GY23_CA_Factsheet.pdf

CaliforniaView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-10T14:14:27.305602
modified: 2026-02-11T15:29:37.449436
access URL: https://ckan.americaview.org/data/factsheets/GY21_California_Factsheet.pdf

CaliforniaView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T02:08:52.374062
modified: 2026-02-11T15:29:37.449032
access URL: https://ckan.americaview.org/data/factsheets/CaliforniaView_2020_Factsheet.pdf

CaliforniaView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-09T19:23:33.904721
modified: 2026-02-11T15:29:37.448621
access URL: https://ckan.americaview.org/data/factsheets/CaliforniaView_2019_Factsheet.pdf

CaliforniaView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-09T19:16:02.548075
modified: 2026-02-11T15:29:37.447319
access URL: https://ckan.americaview.org/data/factsheets/CaliforniaView_2014_Factsheet.pdf

CaliforniaView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-21T19:35:16.108925
modified: 2026-02-11T15:29:37.449842
access URL: https://ckan.americaview.org/data/factsheets/GY22_CA_Factsheet.pdf

An Efficient Workflow and Accuracy Assessment for ICESat-2 and Multispectral Imagery Fusion for Bathymetric Mapping
type: Dataset
description: Interest in mapping bathymetry from multispectral satellite imagery has grown steadily since the 1970s. Notwithstanding the significant advancements made during this time, one limitation is that the group of algorithms collectively referred to as Satellite Derived Bathymetry (SDB) generally require “seed depths,” hindering their use for areas lacking reliable reference data. Recently, NASA’s ICESat-2 ATLAS, a satellite-based, photon counting green lidar, has been shown capable of bathymetric mapping. Although ICESat-2 bathymetry is constrained to discrete tracklines, synergistic fusion of ICESat-2 and multispectral, passive imagery can generate contiguous bathymetry over large spatial extents. Despite these significant recent results and the possibilities they open for filling shallow nearshore data voids worldwide, significant challenges remain, including: 1) ICESat 2/multispectral imagery fusion techniques are still in the early research stage and not efficient enough for widespread operational use for the average user, and 2) detailed, empirical accuracy assessments of fusion-based bathymetric products generated through operationally-efficient workflows have not been conducted. This research addresses these gaps. A highly-efficient workflow is developed and tested for generating bathymetric DEMs for large areas through largely-automated retrieval and fusion of ICESat-2 photon clouds and multispectral imagery, with the outputs disseminated via a webGIS. Empirical accuracy assessments are performed in two locations, selected to encompass vastly differing environmental conditions. One is a freshwater, inland water site where airborne imagery was used, while the other is a tropical, coastal saltwater site, covered by Landsat 8 and Sentinel-2 data. The workflow is shown to be operationally efficient and capable of producing bathymetry of sufficient accuracy for many coastal science applications, with RMSEs for the two sites being 0.95 m for Lake Tahoe (σ = 0.89 m, μ =…
issued: 2026-02-11T15:29:46.466461
modified: 2026-02-11T15:29:46.466465
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Oregon State University, MS thesis
type: Distribution
format: HTML
issued: 2021-09-17T20:06:54.516783
modified: 2026-02-11T15:29:46.458099
access URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/5q47rw315

Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA
type: Dataset
description: This empirical research examines the normalized difference vegetation index (NDVI) and the percent impervious surface area (%ISA) as indicators of urban heat island (UHI) effects, using the relationships between land surface temperature (LST), %ISA, and NDVI. Landsat 8 Operational Land Imager and Thermal Infrared Sensor data were used to estimate the LST in Connecticut at different times. A map of the percent impervious surface was generated using the Impervious Surface Analysis Tool developed by the Center for Land Use Education and Research and distributed through the National Oceanic and Atmospheric Administration. Strong linear relationships between LST and %ISA exist, as stated in previous studies, whereas the relationship between LST and NDVI is evidently affected by the seasons. As the patterns of LST in urban areas are influenced by the UHI, the results demonstrate that %ISA is a more reliable indicator of UHI effects than NDVI. Thus, %ISA could be a promising alternative for use in the quantitative analysis of LST when studying UHI effects.
issued: 2026-02-11T15:29:47.424075
modified: 2026-02-11T15:29:47.424080
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Environmental Earth Sciences volume 79, Article number: 424
type: Distribution
issued: 2021-09-16T16:21:05.339672
modified: 2026-02-11T15:29:47.416640
access URL: https://link.springer.com/article/10.1007/s12665-020-09159-0

Bangladesh - Solar Radiation Measurement Data
type: Dataset
description: Data repository for measurements from a solar measurement station in Bangladesh. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values for solar radiation levels, temperature, air pressure and wind speed. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/
issued: 2025-09-18T20:33:44.529281
modified: 2026-02-19T22:31:01.420224
contact point: Organization
  fn: Joana Zerbin
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://globalsolaratlas.info/

Solar-Measurements_Bangladesh_Feni_WB-ESMAP_Raw
type: Distribution
format: ZIP
issued: 2026-02-09T11:53:47.277719
modified: 2026-02-19T22:31:01.424041
access URL: https://energydata.info/dataset/f23c1086-a5e2-4889-849d-cac110086900/resource/2f96955c-6f0b-48c3-a437-f4d122867b90/download/solar-measurements_bangladesh_feni_wb-esmap_raw.zip

Solar-Measurements-Monthly-Reports_Bangladesh_Feni_WB-ESMAP
type: Distribution
format: ZIP
issued: 2026-02-09T11:55:24.820406
modified: 2026-02-19T22:31:01.424888
access URL: https://energydata.info/dataset/f23c1086-a5e2-4889-849d-cac110086900/resource/98b12ff6-bcb1-4e8b-a4f8-b8be390e5628/download/solar-measurements-monthly-reports_bangladesh_feni_wb-esmap.zip

Solar-Measurements_Bangladesh_Feni_WB-ESMAP_QC
type: Distribution
format: CSV
issued: 2026-02-09T11:48:08.755664
modified: 2026-02-19T22:31:01.423509
access URL: https://energydata.info/dataset/f23c1086-a5e2-4889-849d-cac110086900/resource/a7190145-47c6-4926-b829-bf5c48501d2b/download/solar-measurements_bangladesh_feni_wb-esmap_qc.csv

Solar-Measurements_Bangladesh_Feni_WB-ESMAP_Header
type: Distribution
format: CSV
issued: 2026-02-09T11:54:44.823691
modified: 2026-02-19T22:31:01.424472
access URL: https://energydata.info/dataset/f23c1086-a5e2-4889-849d-cac110086900/resource/d45fd8f0-b2f3-4329-b555-d07cd08a381a/download/solar-measurements_bangladesh_feni_wb-esmap_header.csv

2017 Earth Observation Day Poster
type: Dataset
description: The 2017 Earth Observation Day poster features a mosaic of Landsat 8 satellite images showing the contiguous United States of America on the front and a board game-style exploration of the history and contributions of the Landsat program on the back.
issued: 2026-02-11T15:29:48.514777
modified: 2026-02-11T15:29:48.514783
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

2017 EOD Landsat Poster (back)
type: Distribution
format: PDF
issued: 2021-08-02T15:57:40.088537
modified: 2026-02-11T15:29:48.505457
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_Poster_2017_Back.pdf

2017 EOD Landsat Poster Spanish Version (back)
type: Distribution
format: PDF
issued: 2021-08-02T15:58:53.487415
modified: 2026-02-11T15:29:48.506375
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_Poster_2017_Back_Spanish.pdf

2017 EOD Landsat Poster Spanish Version (front)
type: Distribution
format: PDF
issued: 2021-08-02T15:58:21.476836
modified: 2026-02-11T15:29:48.505955
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_Poster_2017_Front_Spanish.pdf

2017 EOD Landsat Poster (front)
type: Distribution
format: PDF
issued: 2021-08-02T15:56:59.589850
modified: 2026-02-11T15:29:48.504988
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_Poster_2017_Front.pdf

South Asia - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed and wind power density potential for South Asia. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).
issued: 2025-09-18T20:33:46.086384
modified: 2026-02-19T22:31:01.920210
contact point: Organization
  fn: Albertine Potter van Loon
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: http://globalwindatlas.info/

South Asia - Wind Speed and Wind Power Potential Maps
type: Distribution
format: HTML
issued: 2026-02-09T13:23:53.868655
modified: 2026-02-19T22:31:01.923157
access URL: https://globalwindatlas.info/downloads/SOA

The Spatial Variability of NDVI Within a Wheat Field: Information Content and Implications for Yield and Grain Protein Monitoring
type: Dataset
description: Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, his…
issued: 2026-02-11T15:29:49.824713
modified: 2026-02-11T15:29:49.824718
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Website
type: Distribution
issued: 2023-08-07T18:09:06.115354
modified: 2026-02-11T15:29:49.816873
access URL: https://doi.org/10.1371/journal.pone.0265243

KentuckyView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of KentuckyView.
issued: 2026-02-11T15:29:50.614524
modified: 2026-02-11T15:29:50.614527
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

KentuckyView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:29:51.337580
modified: 2026-02-11T15:29:50.605759
access URL: https://ckan.americaview.org/data/factsheets/KentuckyView_2019_Factsheet.pdf

KentuckyView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:28:46.571108
modified: 2026-02-11T15:29:50.604973
access URL: https://ckan.americaview.org/data/factsheets/KentuckyView_2015_Factsheet.pdf

KentuckyView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T16:40:57.691074
modified: 2026-02-11T15:29:50.606909
access URL: https://ckan.americaview.org/data/factsheets/GY22_KY_Factsheet.pdf

KentuckyView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-23T18:50:15.277814
modified: 2026-02-11T15:29:50.606538
access URL: https://ckan.americaview.org/data/factsheets/GY21_Kentucky_Factsheet.pdf

KentuckyView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T13:22:26.569341
modified: 2026-02-11T15:29:50.606160
access URL: https://ckan.americaview.org/data/factsheets/KentuckyView_2020_Factsheet.pdf

KentuckyView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-06T16:14:47.420808
modified: 2026-02-11T15:29:50.607278
access URL: https://ckan.americaview.org/data/factsheets/GY23_KY_Factsheet.pdf

KentuckyView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:28:09.245288
modified: 2026-02-11T15:29:50.604527
access URL: https://ckan.americaview.org/data/factsheets/KentuckyView_2014_Factsheet.pdf

KentuckyView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:29:15.585002
modified: 2026-02-11T15:29:50.605371
access URL: https://ckan.americaview.org/data/factsheets/KentuckyView_2018_Factsheet.pdf

OhioView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of OhioView.
issued: 2026-02-11T15:29:51.617079
modified: 2026-02-11T15:29:51.617084
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

OhioView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:36:00.175098
modified: 2026-02-11T15:29:51.607716
access URL: https://ckan.americaview.org/data/factsheets/OhioView_2019_Factsheet.pdf

OhioView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:34:52.630231
modified: 2026-02-11T15:29:51.606887
access URL: https://ckan.americaview.org/data/factsheets/OhioView_2015_Factsheet.pdf

OhioView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-28T19:40:59.135488
modified: 2026-02-11T15:29:51.608533
access URL: https://ckan.americaview.org/data/factsheets/GY21_Ohio_Factsheet.pdf

OhioView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T17:45:19.421086
modified: 2026-02-11T15:29:51.608962
access URL: https://ckan.americaview.org/data/factsheets/GY22_OH_Factsheet.pdf

OhioView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:34:20.705737
modified: 2026-02-11T15:29:51.606404
access URL: https://ckan.americaview.org/data/factsheets/OhioView_2014_Factsheet.pdf

OhioView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T03:14:16.610627
modified: 2026-02-11T15:29:51.608116
access URL: https://ckan.americaview.org/data/factsheets/OhioView_2020_Factsheet.pdf

OhioView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-03-10T22:41:01.092703
modified: 2026-02-11T15:29:51.609365
access URL: https://ckan.americaview.org/data/factsheets/GY23_OH_Factsheet.pdf

OhioView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T02:35:31.767350
modified: 2026-02-11T15:29:51.607309
access URL: https://ckan.americaview.org/data/factsheets/OhioView_2018_Factsheet.pdf

Utilizing Sentinel-2 Satellite Imagery for Precision Agriculture over Potato Fields in Lebanon
type: Dataset
description: Lebanon has traditionally been a major potato producer with 451,860 tons produced in 2014. Generally, potatoes make up 30% of the total Lebanese agricultural exports where approximately 60% of the potato production is exported to the Arab region, the UK and Brazil. The purpose of this study is to promote precision agriculture techniques in Lebanon that will help local farmers in the central Bekaa Valley with land management decisions. The European Space Agency’s satellite missions Sentinel-2A, launched June 23rd 2015, and the Sentinel-2B, recently launched on March 7th 2017, are multispectral high resolution imaging systems that provide global coverage every 5 days. The Sentinel program is a land monitoring program that includes an aim to improve agricultural practices. The imagery is 13 band data in the visible, near infrared and short wave infrared parts of the electromagnetic spectrum and ranges from 10-20 m including three 60 m bands pixel resolution. Sentinel is freely available data that has the potential to empower farmers with information to respond quickly to maximize crop health. Due to the political and security conflicts in the region, utilizing satellite imagery for Lebanon is more reasonable and realistic than operating Unmanned Aircraft Systems (UAS) for high resolution remote sensing. During the 2017 growing season, local farmers provided detailed information in designated fields on their farming practices, crop health, and pest threats. In parallel, Sentinel-2 imagery was processed to study crop health using the following vegetation indices: Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index and Modified Soil Adjusted Vegetation Index 2. Most Lebanese farmers inherit their land from their parents over generations, and as a result most still use traditional farming techniques for irrigation, where decisions are based on prior generations’ practices. However, with the changes in climate…
issued: 2026-02-11T15:29:52.601836
modified: 2026-02-11T15:29:52.601841
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

ASPRS - Pecora 20 Proceedings
type: Distribution
format: PDF
issued: 2021-09-16T16:54:43.999016
modified: 2026-02-11T15:29:52.593800
access URL: https://www.asprs.org/a/publications/proceedings/Pecora20/Abou_Ali_H.pdf

NevadaView Fact Sheets
type: Dataset
description: Yearly fact sheets about the accomplishments of NevadaView activities.
issued: 2026-02-11T15:29:53.447394
modified: 2026-02-11T15:29:53.447399
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

NevadaView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-03-11T14:21:33.222550
modified: 2026-02-11T15:29:53.439363
access URL: https://ckan.americaview.org/data/factsheets/GY23_NV_Factsheet.pdf

Working with Lidar Using ArcGIS Pro - Video Tutorial Playlist
type: Dataset
description: This video playlist contains 17 short videos. These video tutorials can be viewed as a companion to the ebook "Working with Lidar Using ArcGIS Pro" or they can be viewed as stand-alone tutorials. Each of the 17 short videos target a specific topic, ranging from acquiring lidar data to Classifying Points Using Geoprocessing Tools, to Creating Surface Constraints. Note that these tutorials begin with Chapter 7. Chapters 1-6 are covered in Remote Sensing with ArcGIS Pro video tutorials.
issued: 2026-02-11T15:29:54.258765
modified: 2026-02-11T15:29:54.258769
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Working with Lidar Using ArcGIS Pro - Video Tutorial Playlist
type: Distribution
format: HTML
issued: 2021-11-04T01:32:56.125446
modified: 2026-02-11T15:29:54.251137
access URL: https://www.youtube.com/watch

Iowa Best Management Practices Mapping Handbook
type: Dataset
description: This document provides an overview of the Iowa BMP project and three associated projects. It provides background on the project, information about the data structure and necessary input data, descriptions and images of the conservation practices, management considerations, and additional documents needed for project tracking. This project was made possible because of strong collaboration among different funding partners, including AmericaView and state government agencies as well as private industry.
issued: 2026-02-11T15:29:55.109535
modified: 2026-02-11T15:29:55.109541
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Iowa Best Management Practices Mapping Handbook
type: Distribution
format: PDF
issued: 2021-11-03T02:16:45.672694
modified: 2026-02-11T15:29:55.100981
access URL: https://www.iowaview.org/wp-content/uploads/2018/03/Iowa-Best-Management-Practices-Mapping-Handbook.pdf

Mapping And Quantifying The Spatial Characteristics Of Agricultural Drainage Systems In Red River Valley Of The North
type: Dataset
description: The purpose of the study is to map agricultural drainage systems (ADS) at the watershed scale using remote sensing and GIS techniques and examine the effect of ADS. For achieving the purpose of the study, this study selected the Red River Valley (RRV) of the North in which agriculture is a primary industry at the region. Excessive nutrients, sediment, and pesticide from this agricultural area flow into the Red River throughout subsurface drainages. The ADS aims to remove excessed water from agricultural fields, and this ADS is divided into two systems - uncontrolled drainage system (UCDS) and controlled drainage system (CDS). While UCDS allows water flows to the stream or river through using pipes without controlling water table, CDS regulates water table by an equipped structure that controls the volume of water flows in the agricultural fields. For mapping artificially drained tiles between UCDS and CDS fields in the RRV, this study used DEM to digitize linear, map slope, and to calculate surface area. This study digitized linear maps with eight UCDS and twenty CDS fields and the map contains a digitization of tile drainage locations, drainage system patterns, ADS types, and artificially drained surface areas. In the analysis of the two different ADS systems – i.e., UCDS and CDS, this study obtained the indexes by using NDVI, NDWI, and MSAVI2 provided by PlanetScope imagery. In testing the group difference between UCDS and CDS in the three different indexes is examined by computing Analysis of Variance Analysis (ANOVA). Also, this study postulated CDS is more effective to the healthiness of crops than UCDS does in the ADS system. The results of ANOVA indicated that there is no difference in the spectral indexes analyzed by ADS type (i.e., NDVI, NDWI, and MSAVI2). This result implies that the healthiness of crops is not affected ADS type, at least for the year studied here. The causes of this result, as a limitation, is derived from missing information, which is th…
issued: 2026-02-11T15:29:55.883213
modified: 2026-02-11T15:29:55.883218
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

MS Thesis, University of North Dakota
type: Distribution
issued: 2021-09-16T18:58:46.397615
modified: 2026-02-11T15:29:55.873972
access URL: https://commons.und.edu/theses/3281/

2021 Earth Observation Day Poster
type: Dataset
description: The 2021 poster turns to the theme of “Water Today and for the Future.” This poster contains the creative game experience to help bring understanding to how water resources in America are used and managed for future use. You can download and print the poster and you can play the game online.
issued: 2026-02-11T15:29:56.555221
modified: 2026-02-11T15:29:56.555226
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

2021 EOD Poster Insert - Spanish Version
type: Distribution
format: PDF
issued: 2021-11-05T14:01:51.149041
modified: 2026-02-11T15:29:56.546846
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_2021_poster_Insert_Spanish.pdf

2021 EOD Poster Front - Spanish Version
type: Distribution
format: PDF
issued: 2021-11-05T13:59:29.686882
modified: 2026-02-11T15:29:56.546011
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_2021_poster_front_Spanish.pdf

2021 EOD Poster Back - Spanish Version
type: Distribution
format: PDF
issued: 2021-11-05T14:00:42.069125
modified: 2026-02-11T15:29:56.546443
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_2021_poster_back_Spanish.pdf

Rivers Game Large Format Instructions
type: Distribution
format: PDF
issued: 2025-10-08T17:36:37.553334
modified: 2026-02-11T15:29:56.547644
access URL: https://ckan.americaview.org/data/AmericaView/EOD/2021_EOD_Game_Rules.pdf

2021 EOD Poster Back
type: Distribution
format: PDF
issued: 2021-06-23T19:51:41.790745
modified: 2026-02-11T15:29:56.545173
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_2021_poster_back.pdf

2021 EOD Poster Front
type: Distribution
format: PDF
issued: 2021-06-23T19:51:10.451509
modified: 2026-02-11T15:29:56.544728
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_2021_poster_front.pdf

2021 Earth Observation Day Online Rivers Game
type: Distribution
issued: 2022-03-10T16:37:31.472306
modified: 2026-02-11T15:29:56.547234
access URL: https://tabletopia.com/games/earth-observation-day-2021-board-game

2021 EOD Poster Insert
type: Distribution
format: PDF
issued: 2021-06-23T19:52:47.707971
modified: 2026-02-11T15:29:56.545603
access URL: https://ckan.americaview.org/data/AmericaView/EOD/ESW_2021_poster_Insert.pdf

Assessing the Ability to Quantify Bathymetric Change over Time Using Solely Satellite-Based Measurements
type: Dataset
description: Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique.
issued: 2026-02-11T15:29:57.410688
modified: 2026-02-11T15:29:57.410692
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Website
type: Distribution
issued: 2023-08-07T13:34:48.442371
modified: 2026-02-11T15:29:57.401474
access URL: https://doi.org/10.3390/rs14051232

KansasView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of KansasView.
issued: 2026-02-11T15:29:58.115461
modified: 2026-02-11T15:29:58.115465
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

KansasView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-23T18:48:51.716113
modified: 2026-02-11T15:29:58.107047
access URL: https://ckan.americaview.org/data/factsheets/GY21_Kansas_Factsheet.pdf

KansasView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T02:36:50.043278
modified: 2026-02-11T15:29:58.106666
access URL: https://ckan.americaview.org/data/factsheets/KansasView_2020_Factsheet.pdf

KansasView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:24:55.511735
modified: 2026-02-11T15:29:58.105413
access URL: https://ckan.americaview.org/data/factsheets/KansasView_2015_Factsheet.pdf

KansasView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-02-04T16:27:08.612824
modified: 2026-02-11T15:29:58.107829
access URL: https://ckan.americaview.org/data/factsheets/GY23_KS_Factsheet.pdf

KansasView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:25:26.711757
modified: 2026-02-11T15:29:58.105874
access URL: https://ckan.americaview.org/data/factsheets/KansasView_2018_Factsheet.pdf

KansasView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:24:27.341347
modified: 2026-02-11T15:29:58.104889
access URL: https://ckan.americaview.org/data/factsheets/KansasView_2014_Factsheet.pdf

KansasView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T01:25:53.739584
modified: 2026-02-11T15:29:58.106277
access URL: https://ckan.americaview.org/data/factsheets/KansasView_2019_Factsheet.pdf

KansasView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T16:39:52.738771
modified: 2026-02-11T15:29:58.107422
access URL: https://ckan.americaview.org/data/factsheets/GY22_KS_Factsheet.pdf

TexasView Fact Sheets
type: Dataset
description: Yearly fact sheets highlighting the efforts and successes of TexasView.
issued: 2026-02-11T15:29:59.341661
modified: 2026-02-11T15:29:59.341665
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

TexasView 2023 Student Research Fact Sheet
type: Distribution
format: PDF
issued: 2025-03-12T14:21:38.717642
modified: 2026-02-11T15:29:59.333801
access URL: https://ckan.americaview.org/data/factsheets/GY23_TX_Factsheet_TVREG.pdf

TexasView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-04-16T15:55:57.555288
modified: 2026-02-11T15:29:59.330384
access URL: https://ckan.americaview.org/data/factsheets/TexasView_2018_Factsheet.pdf

TexasView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-04-16T15:56:26.199384
modified: 2026-02-11T15:29:59.330813
access URL: https://ckan.americaview.org/data/factsheets/TexasView_2019_Factsheet.pdf

TexasView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-28T20:08:45.945294
modified: 2026-02-11T15:29:59.331656
access URL: https://ckan.americaview.org/data/factsheets/GY21_Texas_Factsheet.pdf

TexasView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T17:52:41.149822
modified: 2026-02-11T15:29:59.332163
access URL: https://ckan.americaview.org/data/factsheets/GY22_TX_Factsheet.pdf

TexasView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T03:24:57.518252
modified: 2026-02-11T15:29:59.331212
access URL: https://ckan.americaview.org/data/factsheets/TexasView_2020_Factsheet.pdf

TexasView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-04-16T15:55:28.899069
modified: 2026-02-11T15:29:59.329936
access URL: https://ckan.americaview.org/data/factsheets/TexasView_2014_Factsheet.pdf

TexasView 2022 Student Research Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T17:54:31.079296
modified: 2026-02-11T15:29:59.332609
access URL: https://ckan.americaview.org/data/factsheets/GY22_TX_Factsheet_TVREG.pdf

TexasView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-03-12T14:20:46.119873
modified: 2026-02-11T15:29:59.333241
access URL: https://ckan.americaview.org/data/factsheets/GY23_TX_Factsheet.pdf

São Tomé and Príncipe - Multi-Tier Framework (MTF) Survey
type: Dataset
description: The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (which includes raw data sets of dta format), excel spreadsheet, pdf, and docx.
issued: 2025-09-18T20:33:53.786586
modified: 2026-02-19T22:31:03.038533
contact point: Organization
  fn: Marina Brutinel; Yuhan Wang; Bryan Bonsuk Koo; Elisa Portale; Dana Rysankova
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank

MTF_São_Tomé_and_Príncipe_Energy_Access_Country_Diagnostic_Report.pdf
type: Distribution
format: PDF
issued: 2026-02-09T12:18:26.108246
modified: 2026-02-19T22:31:03.043197
access URL: https://energydata.info/dataset/fcccab08-9bab-4cc0-b7a3-6808d429be9f/resource/13c47026-3eae-4521-8df6-96de5089cff1/download/mtf_energy_access_country_diagnostic_report_stp_7.2019.pdf

MTF_São_Tomé_and_Príncipe_Codebook.xlsx
type: Distribution
format: XLSX
issued: 2026-02-09T12:13:40.124324
modified: 2026-02-19T22:31:03.042441
access URL: https://energydata.info/dataset/fcccab08-9bab-4cc0-b7a3-6808d429be9f/resource/48d402cc-5e33-40a3-a475-ccdbe4be9a3f/download/codebook_stp.xlsx

MTF_São_Tomé_and_Príncipe_Questionnaire_Household.pdf
type: Distribution
format: PDF
issued: 2026-02-09T12:14:00.690045
modified: 2026-02-19T22:31:03.042824
access URL: https://energydata.info/dataset/fcccab08-9bab-4cc0-b7a3-6808d429be9f/resource/72b3ceaa-f373-42d1-acc2-f1c81901c970/download/1.-household_stp.pdf

MTF_São_Tomé_and_Príncipe_Sampling_Strategy.pdf
type: Distribution
format: PDF
issued: 2026-02-09T12:13:04.854530
modified: 2026-02-19T22:31:03.042035
access URL: https://energydata.info/dataset/fcccab08-9bab-4cc0-b7a3-6808d429be9f/resource/942d9835-d533-4712-84d6-810453e0784f/download/sampling-strategy_mtf_standard.pdf

MTF_ São_Tomé_and_Príncipe_Raw_Data.dta
type: Distribution
format: Other
issued: 2026-02-09T12:12:09.137803
modified: 2026-02-19T22:31:03.041603
access URL: https://energydata.info/dataset/fcccab08-9bab-4cc0-b7a3-6808d429be9f/resource/9bd4e51d-7318-453e-b419-9a1186763963/download/stp_hh-dataset.dta

Hurricane Season Geospatial Data Mining Workshop
type: Dataset
description: Through the cooperation of the LouisianaView consortium members and co-sponsored with the local USGS liaison, this annual workshop is offered free to everyone interested in up-to-date information on data availability for the geospatial emergency responder. This is a 4-day virtual workshop hosts speakers from multiple Federal, State and Private Response Teams, each presenting their data, websites, links, and contacts while also fielding questions live from those in attendance, proving again and again what a cohesive and informed network of geospatial responders can mean to the inhabitants and economic base within Louisiana, the Gulf of Mexico region and the Caribbean.
issued: 2026-02-11T15:30:00.494221
modified: 2026-02-11T15:30:00.494225
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

2020 Workshop Summary
type: Distribution
format: PDF
issued: 2021-11-11T14:28:15.714564
modified: 2026-02-11T15:30:00.486094
access URL: https://ckan.americaview.org/data/LouisianaView/GY19_LA_RAC_Disaster_Workshop_2020.pdf

2021 Workshop Summary
type: Distribution
format: PDF
issued: 2022-03-09T15:04:34.325222
modified: 2026-02-11T15:30:00.486529
access URL: https://ckan.americaview.org/data/LouisianaView/GY20_LA_RAC_Disaster_Workshop_2021.pdf

Taiwan - Wind Speed and Wind Power Potential Maps
type: Dataset
description: Maps with wind speed, wind rose and wind power density potential in Taiwan. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
issued: 2025-09-18T20:33:54.083426
modified: 2026-02-19T22:31:03.326118
contact point: Organization
  fn: Albertine Potter van Loon
keyword: AmeriGEO
keyword: AmeriGEOSS
keyword: Energy
keyword: GEO
keyword: GEOSS
keyword: Global
keyword: SDG
keyword: SDG7
keyword: World Bank
landing page: https://globalwindatlas.info

Taiwan - Wind Speed and Wind Power Potential Maps - GIS
type: Distribution
format: HTML
issued: 2026-02-09T11:56:41.251040
modified: 2026-02-19T22:31:03.329448
access URL: https://globalwindatlas.info/area/Taiwan

Taiwan - Wind Speed and Wind Power Potential Maps - Posters
type: Distribution
format: HTML
issued: 2026-02-09T11:57:22.498204
modified: 2026-02-19T22:31:03.329977
access URL: https://globalwindatlas.info/downloads/Taiwan

Flash Drought Identification from Satellite-Based Land Surface Water Index
type: Dataset
description: Flash droughts can lead to significant agricultural and ecosystem impacts via rapid land surface desiccation. While gridded weather and climate datasets, land surface models, or widely spaced in situ observations are typically used to quantify flash drought development, coarse spatial data limits the ability to determine fine-scale spatial evolution of flash drought at landscape and ecosystem scales. In this study, a novel approach is introduced to objectively identify flash drought using the land surface water index (LSWI) derived from satellite observations. LSWI is a water-related vegetation index that represents the total water content in vegetation by using the near-infrared and shortwave infrared bands. LSWI was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance (MOD09A1) with a 500 m spatial resolution and an 8-day temporal resolution. When applied to two well-established case studies, LSWI anomalies were able to capture the temporal and spatial evolution of flash drought over Oklahoma during the years of 2011 and 2012. In addition, rapid changes of LSWI during flash drought were comparable across space and time to the reanalysis-based Standardized Evaporative Stress Ratio (SESR), while negative anomalies of LSWI following flash drought corresponded with drought impacts via the United States Drought Monitor. It was found that LSWI was able to identify flash drought with a finer spatial resolution (500 m) and revealed spatial propagation of flash drought events that would not otherwise be seen with coarser meteorological data (e.g., ∼32 km at the lowest latitude for the North American Regional Reanalysis data). Furthermore, LSWI greatly enhanced the ability to detect rapid changes in surface conditions driven by flash drought and was able to provide early warning for drought development when compared with the USDM across Oklahoma. As such, the temporal and spatial evolution of flash drought depicted by LSWI and the…
issued: 2026-02-11T15:30:01.330020
modified: 2026-02-11T15:30:01.330025
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Remote Sensing Applications: Society and Environment
type: Distribution
issued: 2023-07-28T14:57:27.336441
modified: 2026-02-11T15:30:01.321727
access URL: https://www.sciencedirect.com/science/article/pii/S2352938522000787

GEE_1: Google Earth Engine Tutorial Pt. I
type: Dataset
description: Data Acquisition • Acquiring data stored on Google’s servers for use in Google Earth Engine.
issued: 2026-02-11T15:30:02.185508
modified: 2026-02-11T15:30:02.185512
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Data Acquisition
type: Distribution
format: PDF
issued: 2021-11-02T02:21:14.140449
modified: 2026-02-11T15:30:02.177450
access URL: https://drive.google.com/file/d/1DPgCKK8gTZ57gD1eCYdCQvPK3aHyeft0/view

Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping
type: Dataset
description: Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.
issued: 2026-02-11T15:30:02.933262
modified: 2026-02-11T15:30:02.933267
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Website
type: Distribution
issued: 2023-08-07T13:37:00.085367
modified: 2026-02-11T15:30:02.925653
access URL: https://doi.org/10.3390/rs14153622

Kansas University Field Station NEON Data Viewer
type: Dataset
description: KansasView also developed an AGOL web mapping application to provide users the ability to explore and visualize the National Science Foundation’s National Earth Observation Network (NEON) Airborne Observation Platform (AOP) remote sensing data collected at the KU Field Station (KUFS) along with Sentinel2, Landsat 8, and aerial image services (mid right). KansasView previously supported processing of tiled NEON AOP data to create data mosaics that were then made available through the online tool.
issued: 2026-02-11T15:30:03.760931
modified: 2026-02-11T15:30:03.760935
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Online Viewer
type: Distribution
issued: 2023-01-25T14:31:42.408039
modified: 2026-02-11T15:30:03.752062
access URL: https://ku.maps.arcgis.com/apps/webappviewer/index.html

VermontView Fact Sheets
type: Dataset
description: Yearly fact sheets that highlight the efforts and success of VermontView.
issued: 2026-02-11T15:30:04.467936
modified: 2026-02-11T15:30:04.467941
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

VermontView 2019 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T03:04:06.071647
modified: 2026-02-11T15:30:04.457544
access URL: https://ckan.americaview.org/data/factsheets/VermontView_2019_Factsheet.pdf

VermontView 2021 Fact Sheet
type: Distribution
format: PDF
issued: 2023-02-28T21:15:59.305480
modified: 2026-02-11T15:30:04.458353
access URL: https://ckan.americaview.org/data/factsheets/GY21_Vermont_FactSheet.pdf

VermontView 2023 Fact Sheet
type: Distribution
format: PDF
issued: 2025-03-12T14:28:08.975560
modified: 2026-02-11T15:30:04.459345
access URL: https://ckan.americaview.org/data/factsheets/GY23_VT_Factsheet.pdf

VermontView 2022 Fact Sheet
type: Distribution
format: PDF
issued: 2024-02-22T17:56:26.809586
modified: 2026-02-11T15:30:04.458757
access URL: https://ckan.americaview.org/data/factsheets/GY22_VT_Factsheet.pdf

VermontView 2015 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T03:03:12.901066
modified: 2026-02-11T15:30:04.456700
access URL: https://ckan.americaview.org/data/factsheets/VermontView_2015_Factsheet.pdf

VermontView 2014 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T03:02:39.692338
modified: 2026-02-11T15:30:04.456261
access URL: https://ckan.americaview.org/data/factsheets/VermontView_2014_Factsheet.pdf

VermontView 2020 Fact Sheet
type: Distribution
format: PDF
issued: 2022-04-13T03:28:32.321773
modified: 2026-02-11T15:30:04.457940
access URL: https://ckan.americaview.org/data/factsheets/VermontView_2020_Factsheet.pdf

VermontView 2018 Fact Sheet
type: Distribution
format: PDF
issued: 2021-09-14T03:03:42.132397
modified: 2026-02-11T15:30:04.457125
access URL: https://ckan.americaview.org/data/factsheets/VermontView_2018_Factsheet.pdf

Fundamentals of Image Analysis in Google Earth Engine
type: Dataset
description: Cloud-based image processing platforms like the Google Earth Engine (GEE) bring unprecedented possibilities for education, research, and outreach. This workshop will focus on an interactive exploration of GEE capabilities, the repository of all of publicly available aerial and satellite data, and user upload of imagery for analysis. The workshop will begin with a presentation of examples of GEE projects with a focus on education, undergraduate research, and outreach followed by hands-activities.
issued: 2026-02-11T15:30:05.464742
modified: 2026-02-11T15:30:05.464747
keyword: afrigeo
keyword: afrigeoss
keyword: americaview
keyword: amerigeo
keyword: amerigeoss
keyword: ckan
keyword: eurogeo
keyword: eurogeoss
keyword: geo
keyword: geoss
keyword: global
keyword: remote-sensing

Workshop at ASPRS Annual Conference (Denver Jan 2019)
type: Distribution
issued: 2021-09-16T19:06:53.636325
modified: 2026-02-11T15:30:05.456754
access URL: http://conferences.asprs.org/class/465/

World Bank
type: Agent

AmericaView
type: Agent