Training and validation data from the AI for Critical Mineral Assessment Competition (ver. 2.0, July 2025)
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 competition are provided here, as well as competition details and baseline solutions. The data are 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.
First Posted - December 27, 2023
Revised - July 21, 2025
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Margaret A Goldman",
"@type": "vcard:Contact",
"hasEmail": "mailto:mgoldman@usgs.gov"
}
|
| description | 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 competition are provided here, as well as competition details and baseline solutions. The data are 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. First Posted - December 27, 2023 Revised - July 21, 2025 |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P9FXSPT1",
"mediaType": "application/http",
"description": "Landing page for access to the data"
},
{
"@type": "dcat:Distribution",
"title": "Original Metadata",
"format": "XML",
"mediaType": "text/xml",
"description": "The metadata original format",
"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.63a100d6d34e0de3a1f2794f.xml"
}
]
|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_63a100d6d34e0de3a1f2794f |
| keyword |
[
"AI",
"Artificial Intelligence",
"GIS",
"ML",
"USGS:63a100d6d34e0de3a1f2794f",
"competition",
"critical mineral resources",
"digitization",
"economy",
"geological maps",
"geoscientificInformation",
"geospatial datasets",
"machine learning",
"modeling",
"resource assessment",
"tool development",
"topographic maps",
"training data"
]
|
| modified | 2025-07-25T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -178.2, 6.6, -49.0, 83.3 |
| theme |
[
"geospatial"
]
|
| title | Training and validation data from the AI for Critical Mineral Assessment Competition (ver. 2.0, July 2025) |
