figshare
Browse

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

dataset
posted on 2023-03-22, 17:59 authored by Aaron MaxwellAaron Maxwell

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


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).


surficialDL

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

alluvDL: 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.  

tillDL: 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.

Funding

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

History

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC