Map feature extraction 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 feature extraction 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.
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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 map feature extraction 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. |
| 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.64c14e71d34e70357a329912.xml"
}
]
|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_64c14e71d34e70357a329912 |
| keyword |
[
"AI",
"Artificial Intelligence",
"GIS",
"ML",
"USGS:64c14e71d34e70357a329912",
"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 | Map feature extraction challenge training and validation data |
