surficialDL: A geomorphology deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA

<strong>surficialDL: A geomorpholgy deep learning dataset of alluvium and thick glacial till derived form 1:24,000 scale surficial geology data for the western portion of Massachusetts, USA</strong> <br> <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>surficialDL</strong> The digital terrain model associated with these data/project is available here: https://s3.us-east-1.amazonaws.com/download.massgis.digital.mass.gov/lidar/LIDAR_DEM_32BIT_FP.gdb.zip. <strong>alluvDL</strong>: polygons (vectors folder) and extents (extents folder) for alluvium features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology. <strong>tillDL</strong>: polygons (vector folder) and extents (extents folder) for thick till features separated into training, validation, and testing partitions. These data were derived from the 1:24,000 scale Massachusetts Surficial Geology dataset: https://www.mass.gov/info-details/massgis-data-usgs-124000-surficial-geology.