Below are links to data and/or code associated with some of our publications or projects. If you use these datasets, please cite our papers. Also, feel free to report any issues.

  1. WV LiDAR and Elevation Download Tool: Web app that allows users to download LiDAR data and derived products collected within the state of West Virginia.
  2. wvLC2016: 2016 land cover dataset for West Virginia derived from National Agriculture Imagery Program (NAIP) orthophotography using geographic objected-based image analysis and machine learning. This product is associated with the following publication: "Large-Area, High Spatial Resolution Land Cover Mapping using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations".
  3. ctWhgtAcc: R-based assessment tool, instructions, and example data associated with "Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics".
  4. topoDL: Data associated with "Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps". The associated code can be found on our GitHub page. These data are used in our Geospatial Deep Learning course.
  5. vfillDL: Data associated with "Mapping the Topographic Features of Mining-Related Valley Fills using Mask R-CNN Deep Learning and Digital Elevation Data". These data are used in our Geospatial Deep Learning course.
  6. wvlcDL_5m: Image chips derived from manually digitized training data and NAIP orthophotography for use in deep learning-based semantic segmentation experiments. This dataset represents an incomplete training set where each cell is not mapped to a land cover class. These data are used in our Geospatial Deep Learning course.
  7. slidesML: Data associated with our GitHub slope failure probabilistic occurrence mapping repo, which provides example code in Python and R.
  8. GEE Extract Example: Code example demonstrating how to use the geemap python library to extract pixel values at point locations for a timeseries of images using Google Earth Engine. A video that walks through the process is available here.
  9. slidesEBM: Data and code associated with "Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling".

GitHub Repos

Semantic Segmentation Topo Maps

Centered Weighted Accuracy Assessment

ML-Based Slope Failure Modeling

DL Examples