Map feature extraction challenge training and validation data
Dates
Publication Date
2023-12-27
Start Date
2022
End Date
2023
Citation
Goldman, M.A., Rosera, J.M., Lederer, G.W., Graham, G.E., Mishra, A., and Yepremyan, A., 2023, Training and validation data from the AI for Critical Mineral Assessment Competition: U.S. Geological Survey data release, https://doi.org/10.5066/P9FXSPT1.
Summary
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 and validation 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 [...]
Summary
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 and validation 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.
The training and validation data were provided to participants in the DARPA AI for Critical Mineral Assessment Competition to develop automated map georeferencing and feature extraction tools, two tasks that the USGS currently does manually using GIS software. The USGS prepares a wide range of maps (various scales, resolutions, and themes) when conducting mineral resource assessments. Manually performing these tasks is time- and labor-intensive. The purpose of the competition was to find innovative approaches to automatically georeference and extract features from diverse maps, with the overall goal of accelerating advances in critical mineral resource science and assessment.
