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

scripts.zip

arcgisTools.atbx:

terrainDerivatives: 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).

rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. 

makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool).

merge_logs.R: R script to merge training logs into a single file. 

predictToExtents.ipynb: Python notebook to use trained model to predict to new data. 

trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library.

graphs_results.R: R code to make graphs with ggplot2 to summarize results.

makeChipsList.R: R code to generate lists of chips in a directory.

makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

vfillDL.zip

dems: 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/) .

extents: extents of the training, testing, and validation areas. These extents were defined by the researchers.

vectors: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.