<p dir="ltr">mineBenchDLsource Dataset</p><p dir="ltr">Source digital terrain model and vector mine bench features for mineBenchDL dataset. New land surface parameters can be derived from the digital elevation data. </p><p dir="ltr">*All layers in NAD83 UTM Zone 17N</p><p dir="ltr">trainQQs.csv: quarter quads randomly selected as training set (69 quarter quads)</p><p dir="ltr">testQQs.csv: quarter quads randomly selected as test set (49 quarter quads)</p><p dir="ltr">valQQs.csv: quarter quads randomly selected as validation set (50 quarter quads)</p><p dir="ltr">vectors folder:</p><p dir="ltr"> mineBenches.shp = digitized mine bench features (QKEY field used as unique ID)</p><p dir="ltr"> quarterQuads.shp = quarter quad boundaries (just those containing mine benches)</p><p dir="ltr"> countyBounds.shp = study area extent in West Virginia</p><p dir="ltr">elev folder:</p><p dir="ltr"> 2 m spatial resolution digital elevation model. Derived from 1 m spatial resolution lidar-derived</p><p dir="ltr"> digital terrain model using pixel aggregation and the mean cell value. </p><p><br></p>
Funding
National Science Foundation (Federal Award ID No. 2046059: “CAREER: Mapping Anthropocene Geomorphology with Deep Learning, Big Data Spatial Analytics, and LiDAR”)