Rate My Professor Aaron Maxwell

Maxwell's research specializes in spatial predictive modeling, geohazard mapping, and applications of machine and deep learning in remote sensing and geospatial science. Current projects address deep learning assessment in remote sensing, LiDAR-based forest fuel estimation, slope failure mapping, geomorphic feature extraction, karst mapping, flood resiliency with participatory GIS, and synthetic 3D data generation. In 2021, he received an NSF CAREER Award ($636,785 over five years) to map 60 years of West Virginia landscape changes, build landslide inventories and models, and develop K-12 data science education in R and Python. He authored Supervised Learning in Remote Sensing (Elsevier, 2025) and influential papers including "Land-surface parameters for spatial predictive mapping and modeling" (Earth-Science Reviews, 2022), "Semantic segmentation deep learning for extracting surface mine extents from historic topographic maps" (Remote Sensing, 2020), and "geodl: An R package for geospatial deep learning semantic segmentation using torch and terra" (PLOS ONE, 2024). With over 4,400 ResearchGate citations, his contributions enhance reproducibility, accuracy in remote sensing, and geohazard management in Appalachia. Maxwell is GISP-certified, Faculty Director of the West Virginia GIS Technical Center, and AmericaView Board Director (2025-2028).