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Eberly College of Arts & Sciences Department of Geology and Geography
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Aaron Maxwell

Assistant Professor of Geography

Contact

Aaron.Maxwell@mail.wvu.edu Website

Profiles and Info

View CV Google Scholar ResearchGate

Categorized As

Role: Faculty,
Focus or Research Area: Geography,

Spatial Analysis, Remote Sensing, Machine/Deep Learning, Natural Hazards, Geomorphology —Dr Maxwell’s research interests are in spatial predictive modeling, geohazard mapping and modeling, applications of machine learning and deep learning in remote sensing and geospatial science, and thematic map accuracy assessment best practices.

Current and ongoing research projects

  • Best practices for assessing deep learning output in remote sensing
  • Forest fuel load estimation with terrestrial LiDAR and machine learning regression
  • Slope failure probabilistic mapping using LiDAR and random forests machine learning
  • Extracting geomorphic features from LiDAR data using deep learning
  • Surficial karst mapping with digital terrain data and deep learning
  • Community resiliency to riverine flooding and participatory GIS
  • 3D GIS and synthetic data generation.
  • Learn more about Dr. Maxwell's  geospatial teaching and research here.

Representative papers

  1. Bester MS, AE Maxwell, I Nealey, MR Gallagher, NS Skowronski, BE McNeil, 2023. Synthetic forest stands and point clouds for model selection and feature space comparison, Remote Sensing, 15(18): 4407.
  2. Maxwell AE, BT Wilson, JJ Holgerson, MS Bester, 2023. Comparing harmonic regression and GLAD phenology metrics for estimation of forest community types and aboveground live biomass within Forest Inventory and Analysis plots, International Journal of Applied Earth Observation and Geoinformation, 122: 103435.
  3. Maxwell AE, WE Odom, CM Shobe, DH Doctor, MS Bester, T Ore, 2023. Exploring the influence of input feature space on CNN-based geomorphic feature extraction from digital terrain data, Earth and Space Science, 10: e2023EA002845.
  4. Maxwell AE, CM Shobe, 2022. Land-surface parameters for spatial predictive mapping and modeling, Earth-Science Reviews, 226: 103944.
  5. Maxwell AE, MS Bester, LA Guillen, CA Ramezan, DJ Carpinello, Y Fan, FM Hartley, SM Maynard, JL Pyron, 2020. Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps, Remote Sensing, 12(24): 4145.
  6. Maxwell AE, MS Beste, LA Guillen, CA Ramezan, DJ Carpinello, Y Fan, FM Hartley, SM Maynard,
    JL Pyron, 2020. Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps, Remote Sensing, 12(24): 4145. https://doi.org/10.3390/rs12244145.
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