vfillDL: A geomorpholgy deep learning dataset of valley fill faces resulting from mountaintop removal coal mining (southern West Virginia, eastern Kentucky, and southwestern Virginia, USA)

<strong>scripts.zip</strong> <br> <strong>arcgisTools.atbx:</strong> <strong>terrainDerivatives</strong>: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). <strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background). <br> <strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. <strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). <strong>merge_logs.R</strong>: R script to merge training logs into a single file. <strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. <strong>trainExperiments.ipynb</strong>: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. <strong>assessmentExperiments.ipynb</strong>: Python code to generate assessment metrics using PyTorch and the torchmetrics library. <strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results. <strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory. <strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool). <br> <strong>vfillDL.zip</strong> <br> <strong>dems</strong>: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (https://www.usgs.gov/3d-elevation-program) and the WV GIS Technical Center (https://wvgis.wvu.edu/) . <strong>extents</strong>: extents of the training, testing, and validation areas. These extents were defined by the researchers. <strong>vectors</strong>: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.