Critical Minerals Map Feature Extraction Using Deep Learning

Critical minerals play a significant role in various areas such as national security, economic growth, renewable energy development, and infrastructure. The assessment of critical minerals requires examining historical scanned maps. The traditional processes of analyzing these scanned maps are labor-intensive, time-consuming, and prone to errors. In this study, we introduce a deep learning technique to help assess critical minerals by automatically extracting digital features from scanned maps. Polygon feature extraction is essential for evaluating the concentration and abundance of critical minerals. The extracted polygon features can be used to update existing geospatial databases, conduct further analysis, and support decision-making processes. The proposed U-Net model takes a six-channel array as input, where the legend feature is concatenated with the map image and serves as a prompt, and the model can generate image segmentation based on arbitrary prompts at test time. Our study shows that the modified U-Net model can effectively extract the mining-related polygon regions based on features listed in legends from historic topographic maps. The model achieves a median F1-score of 0.67. This study has the potential to significantly reduce the time and effort involved in manually digitizing geospatial data from historical topographic maps, thus streamlining the overall assessment process.