Map georeferencing challenge training and validation data

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training, validation, and evaluation data from the map georeferencing challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

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-64c14e4fd34e70357a32990f
metadata_type geospatial
modified 2025-07-25T00:00:00Z
old-spatial -178.2, 6.6, -49.0, 83.3
publisher U.S. Geological Survey
resource-type Dataset
source_datajson_identifier true
source_hash 0ec731ce55ac3ded998944f152081b8d23c7c2130d5be9db4f6b324c08cd3598
source_schema_version 1.1
spatial {"type": "Polygon", "coordinates": [[[-178.2, 6.6], [-178.2, 83.3], [ -49.0, 83.3], [ -49.0, 6.6], [-178.2, 6.6]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • ai
  • artificial-intelligence
  • competition
  • critical-mineral-resources
  • digitization
  • economy
  • geological-maps
  • geoscientificinformation
  • geospatial-datasets
  • gis
  • machine-learning
  • ml
  • modeling
  • resource-assessment
  • tool-development
  • topographic-maps
  • training-data
  • usgs-64c14e4fd34e70357a32990f
isopen False
license_id notspecified
license_title License not specified
maintainer Margaret A Goldman
maintainer_email mgoldman@usgs.gov
metadata_created 2025-09-23T15:58:06.720631
metadata_modified 2025-09-23T15:58:06.720638
notes Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training, validation, and evaluation data from the map georeferencing challenge are provided here, as well as competition details and a baseline solution. The data were derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.
num_resources 2
num_tags 26
title Map georeferencing challenge training and validation data