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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)

Version 2 2023-03-22, 18:00
Version 1 2023-03-22, 17:44
dataset
posted on 2023-03-22, 18:00 authored by Aaron MaxwellAaron Maxwell
<p><strong>scripts.zip</strong></p> <p><br></p> <p><strong>arcgisTools.atbx:</strong></p> <p><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).</p> <p><strong>rasterizeFeatures</strong>: convert vector polygons to raster masks (1 = feature, 0 = background).</p> <p><br></p> <p><strong>makeChips.R</strong>: R function to break terrain derivatives and chips into image chips of a defined size. </p> <p><strong>makeTerrainDerivatives.R</strong>: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool).</p> <p><strong>merge_logs.R</strong>: R script to merge training logs into a single file. </p> <p><strong>predictToExtents.ipynb</strong>: Python notebook to use trained model to predict to new data. </p> <p><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.</p> <p><strong>graphs_results.R</strong>: R code to make graphs with ggplot2 to summarize results.</p> <p><strong>makeChipsList.R</strong>: R code to generate lists of chips in a directory.</p> <p><strong>makeMasks.R</strong>: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).</p> <p><br></p> <p><strong>vfillDL.zip</strong></p> <p><br></p> <p><strong>dems</strong>: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (<a href="https://www.usgs.gov/3d-elevation-program" target="_blank">https://www.usgs.gov/3d-elevation-program</a>) and the WV GIS Technical Center (<a href="https://wvgis.wvu.edu/" target="_blank">https://wvgis.wvu.edu/</a>) .</p> <p><strong>extents</strong>: extents of the training, testing, and validation areas. These extents were defined by the researchers.</p> <p><strong>vectors</strong>: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers. </p>

Funding

National Science Foundation (Federal Award ID No. 2046059: “CAREER: Mapping Anthropocene Geomorphology with Deep Learning, Big Data Spatial Analytics, and LiDAR”)

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