GeoNatShapes: a natural feature reference dataset for mapping and AI training

These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA.

Data and Resources

Field Value
accessLevel public
bureauCode {010:12}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_@id https://ddi.doi.gov/usgs-data.json
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
identifier http://datainventory.doi.gov/id/dataset/usgs-5ec6aad082ce476925eddbc6
metadata_type geospatial
modified 2020-08-27T00:00:00Z
old-spatial -169.8, -14.4, -66.15, 63.0
publisher U.S. Geological Survey
resource-type Dataset
source_datajson_identifier true
source_hash 5ffc86685ba37aaf3ecd73b5d194e6fda432bc50c900362909a2d13c810c1be4
source_schema_version 1.1
spatial {"type": "Polygon", "coordinates": [[[-169.8, -14.4], [-169.8, 63.0], [ -66.15, 63.0], [ -66.15, -14.4], [-169.8, -14.4]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • alabama
  • alaska
  • american-samoa
  • arizona
  • arkansas
  • basin
  • bays
  • california
  • colorado
  • connecticut
  • craters
  • deep-learning
  • delaware
  • florida
  • geoai
  • geography
  • georgia
  • geoscientificinformation
  • geospatial-artificial-intelligence
  • geospatial-datasets
  • hawaii
  • idaho
  • illinois
  • imagerybasemapsearthcover
  • indiana
  • iowa
  • islands
  • kansas
  • kentucky
  • lakes
  • louisiana
  • maine
  • maryland
  • massachusetts
  • michigan
  • minnesota
  • mississippi
  • missouri
  • montana
  • natural-features
  • nebraska
  • nevada
  • new-hampshire
  • new-jersey
  • new-mexico
  • new-york
  • north-carolina
  • north-dakota
  • object-detection
  • ohio
  • oklahoma
  • oregon
  • pennsylvania
  • ridges
  • south-carolina
  • south-dakota
  • tennessee
  • texas
  • training-data
  • united-states-of-america
  • usgs-5ec6aad082ce476925eddbc6
  • utah
  • valleys
  • vermont
  • virginia
  • washington
  • west-virginia
  • wisconsin
  • wyoming
isopen False
license_id notspecified
license_title License not specified
maintainer Samantha T Arundel
maintainer_email sarundel@usgs.gov
metadata_created 2025-09-24T15:07:33.275188
metadata_modified 2025-09-24T15:07:33.275194
notes These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA.
num_resources 1
num_tags 77
title GeoNatShapes: a natural feature reference dataset for mapping and AI training