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 e Risorse

Campo Valore
accessLevel public
bureauCode {010:12}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
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 USGS:5ec6aad082ce476925eddbc6
metadata_type geospatial
modified 20200827
old-spatial -169.8, -14.4, -66.15, 63.0
publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
source_datajson_identifier true
source_hash d50a6dbff15b743ed128e8452f6fb419470f039e
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}
Gruppi
  • AmeriGEOSS
  • National Provider
  • North America
Tag
  • alabama
  • alaska
  • american-samoa
  • amerigeo
  • amerigeoss
  • arizona
  • arkansas
  • basin
  • bays
  • california
  • ckan
  • colorado
  • connecticut
  • craters
  • deep-learning
  • delaware
  • florida
  • geo
  • geoai
  • geography
  • georgia
  • geoscientificinformation
  • geospatial-artificial-intelligence
  • geospatial-datasets
  • geoss
  • hawaii
  • idaho
  • illinois
  • imagerybasemapsearthcover
  • indiana
  • iowa
  • islands
  • kansas
  • kentucky
  • lakes
  • louisiana
  • maine
  • maryland
  • massachusetts
  • michigan
  • minnesota
  • mississippi
  • missouri
  • montana
  • national
  • natural-features
  • nebraska
  • nevada
  • new-hampshire
  • new-jersey
  • new-mexico
  • new-york
  • north-america
  • north-carolina
  • north-dakota
  • object-detection
  • ohio
  • oklahoma
  • oregon
  • pennsylvania
  • ridges
  • south-carolina
  • south-dakota
  • tennessee
  • texas
  • training-data
  • united-states
  • 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 2025-11-20T18:13:13.429543
metadata_modified 2025-11-20T18:13:13.429547
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 2
num_tags 77
title GeoNatShapes: a natural feature reference dataset for mapping and AI training