Prof. Maxwell

I am currently an Associate Professor in the Department of Geology and Geography at West Virginia University. I teach geospatial science courses for both undergraduate and graduate students. I am also the director of West Virginia View, a consortium of public, private, and non-profit remote sensing organizations in West Virginia, and a faculty director of the West Virginia GIS Technical Center. Prior to coming to West Virginia University, I was an Assistant Professor at Alderson Broaddus University. Prior to teaching, I worked as a Remote Sensing Analyst at the Natural Resource Analysis Center (NRAC) at West Virginia University.

I am a graduate of Alderson Broaddus where I received bachelor degrees in Biology, Chemistry, and Environmental Science. I then attended West Virginia University where I earned a master degree in Geology followed by a PhD in Geology. I also hold a Geographic Information Systems Professional (GISP) certification from the GIS Certification Institute.

The primary objectives of my work are to investigate computational methods to extract useful information from geospatial data to make informed decisions and to train students to be effective and thoughtful geospatial scientists and professionals.

Prof. Maxwell

CV

WVU Geol. and Geog.

WVGISTC

Projects

  • Extracting geomorphic features from LiDAR data using deep learning
  • Sinkhole extraction from digital terrain data using deep learning-based semantic segmentation and digital terrain data
  • Best practices for assessing deep learning output in remote sensing
  • Forest fuel load estimation with terrestrial LiDAR and machine learning regression
  • Generation of synthetic forest plots for use in predictive modeling
  • Community flood resiliency in West Virginia
  • Development of an R package for deep learning-based semantic segmentation applied to geospatial data (geodl)

Teaching

  • Geography 350/550: Introduction to GIScience
  • Geography 455/655: Introduction to Remote Sensing
  • Geography 456: Remote Sensing Applications
  • Geography 457/657: Open-Source Spatial Analytics
  • Geography 461/663: Client-Side Web GIS
  • Geography 462/662: Digital Cartography
  • Geography 520: Methods in Open Science
  • Geography 551: Open-Source GIScience

Research Interest

  • Spatial predictive modeling (classification and probabilistic prediction)
  • Accuracy assessment of remote sensing products
  • Application of machine learning, deep learning, and convolutional neural networks (CNNs) in the geospatial sciences
  • Deep learning semantic segmentation and object detection
  • Digital terrain analysis and LiDAR
  • Geographic object-based image analysis (GEOBIA)
  • High spatial resolution land cover mapping and assessment
  • Geomorphic mapping and modeling
  • Wetland and forest type classification
  • Synthetic data generation

Graduate Students

  • Behnam Solouki (Geography MA): TBD
  • Muntasir Tabasum (Geography PhD): TBD
  • Matt Wozniak (Geography PhD): TBD
  • Sarah Farhadpour (Geography PhD): TBD
  • Sara Lusher (Geography MA): Impact of flood zone boundary uncertainty on building-level risk assessment
  • Shannon Maynard (Geology MS): TBD
  • Muhammad Ali (Geology MS, completed): Slope stability below historic pre-law mine benches in the northern coalfields of West Virginia
  • Shobha Yadav (Geography PhD, completed): Linkages between atmospheric circulation, weather, climate, land cover and social dynamics of the Tibetan Plateau
  • Faith Hartley (Geography MA, completed): Using Landsat-based phenology metrics, terrain variables, and machine learning for mapping and probabilistic prediction of forest community types in West Virginia
  • Jaimee Pyron (Geography MA, completed): Wetland mapping using machine learning, Sentinel-1, and digital terrain data
  • Caleb Malay (Geography MA, completed): Comparison of slope failure predictive models in different physiographic regions
  • Hartford Johnson (Geography MA, completed): Assessing fire recovery in California using time series analysis and the Landsat data archive

Publications

  1. Ramezan, C.A., A.E. Maxwell, and J.T. Meadows, 2024. An analysis of qualifications and requirements for geographic information systems (GIS) positions in the United States, Transactions in GIS. https://doi.org/10.1111/tgis.13176.
  2. Farhadpour, S., T.A. Warner, and A.E. Maxwell, 2024. Selecting and interpreting Multiclass loss and accuracy assessment metrics for classifications with class imbalance: guidance and best practices, Remote Sensing, 16(3): 533. https://doi.org/10.3390/rs16030533.
  3. Yadav, S.K., and A.E. Maxwell, (2023). Exploring NDVI change patterns across the Tibetan Plateau at the hillslope scale using geomorphons, International Journal of Remote Sensing 44(23): 7543-7569. https://doi.org/10.1080/01431161.2023.2287561.
  4. Bower, S.J., C.M. Shobe, A.E. Maxwell, and B. Campforts, (2024). The uncertain future of mountaintop-removal-mined landscapes 2: Modeling the influence of topography and vegetation, Geomorphology 446: 108984. https://doi.org/10.1016/j.geomorph.2023.108985.
  5. Shobe, C.M., S.J. Bower, A.E. Maxwell, R.C. Glade, and N.M. Samassi, 2024. The uncertain future of mountaintop-removal-mined landscapes 1: How mining changes erosion processes and variables, Geomorphology 445(15): 108984. https://doi.org/10.1016/j.geomorph.2023.108984.
  6. Bester, M.S., A.E. Maxwell, I. Nealey, M.R. Gallagher, N.S. Skowronski, and B.E. McNeil, 2023. Synthetic forest stands and point clouds for model selection and feature space comparison, Remote Sensing, 15(18): 4407. https://doi.org/10.3390/rs15184407.
  7. Maxwell, A.E., B.T. Wilson, J.J. Holgerson, and M.S. 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. https://doi.org/10.1016/j.jag.2023.103435.
  8. Loudermilk, E.L., S. Pokswinski, C.M. Hawley, A. Maxwell, M.R. Gallagher, N.S. Skowronski, A.T. Hudak, C. Hoffman, and J.K. Hiers, 2023. Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned Southeastern U.S. ecosystem, Fire, 6(4): 151. https://doi.org/10.3390/fire6040151.
  9. Maxwell, A.E., W.E. Odom, C.M. Shobe, D.H. Doctor, M.S. Bester, and 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. https://doi.org/10.1029/2023EA002845.
  10. Maxwell, A.E., M.R. Gallagher, N. Minicuci, M.S. Bester, E.L. Loudermilk, S.M. Pokswinski, and N.S. Skowronski, 2023. Impact of reference data sampling density for estimating plot-level average shrub heights using terrestrial laser scanning, Fire, 6(98): 6030098. https://doi.org/10.3390/fire6030098.
  11. Maxwell, A.E., M.S. Bester, and C.A. Ramezan, 2022. Enhancing reproducibility and replicability in remote sensing deep learning research and practice, Remote Sensing, 14(22): 5760. https://doi.org/10.3390/rs14225760.
  12. Yesenchak, R., S. Sharma, and A.E. Maxwell, 2022. Modes of occurrence, elemental relationships, and economic viability of rare earth elements in West Virginia coals: A statistical approach, Minerals, 12(8): 1060. https://doi.org/10.3390/min12081060.
  13. Warner, T.A., Miller, T.A., La Puma, I.P., Nolan, L.A., Skowronski, N.S., and Maxwell, A.E., 2022. Exploring golden eagle habitat preference using lidar-based canopy bulk density. Remote Sensing Letters, 13(6), 556-567. https://doi.org/10.1080/2150704X.2022.2055985
  14. Maxwell, A.E. and C.M. Shobe, 2022. Land-surface parameters for spatial predictive mapping and modeling, Earth Science Reviews, 226: 103944. https://doi.org/10.1016/j.earscirev.2022.103944.
  15. Hartley, F.M., A.E. Maxwell, R.E. Landenberger, and Z.J. Bortlot, 2022. Forest type differentiation using GLAD phenology metrics, terrain variables, and machine learning, Geographies, 2(3): 491-515. https://doi.org/10.3390/geographies2030030.
  16. Maxwell, A.E., M. Sharma, and K.A. Donaldson, 2021. Explainable boosting machines for slope failure spatial predictive modeling, Remote Sensing, 13(24): 4991. https://doi.org/10.3390/rs13244991.
  17. Gallagher, M.R., A.E. Maxwell, L.A. Guillen, A. Everland, E.L. Loudermilk, and N.S. Skowronski, 2021. Estimation of plot-level burn severity using terrestrial laser scanning, Remote Sensing, 13(20): 4168. https://doi.org/10.3390/rs13204168.
  18. Cribari, V., M.P. Strager, A.E. Maxwell, and C. Yuill, 2021. Landscape changes in the southern coalfields of West Virginia: Multi-level intensity analysis and surface mining transitions in the headwaters of the Coal River from 1976 to 2016, Land, 10(7): 748. https://doi.org/10.3390/land10070748.
  19. Maxwell, A.E., T.A. Warner, and L.A. Guillen, 2021. Accuracy assessment in convolutional neural network-based deep learning remote sensing studies – Part 2: Recommendations and best practices, Remote Sensing, 13(13): 2591. https://doi.org/10.3390/rs13132591
  20. Maxwell, A.E., T.A. Warner, and L.A. Guillen, 2021. Accuracy assessment in convolutional neural network-based deep learning remote sensing studies – Part 1: Literature review, Remote Sensing, 13(13): 2450. https://doi.org/10.3390/rs13132450
  21. Maxwell, A.E., M. Sharma, J.S. Kite, K.A. Donaldson, S.M. Maynard, and C.M. Malay, 2021. Assessing the generalization of machine learning-based slope failure prediction to new geographic extents, ISPRS International Journal of Geo-Information, 10(5): 293. https://doi.org/10.3390/ijgi10050293.
  22. Higgins, A.K. and A.E. Maxwell, 2021. Universal design for learning in the geosciences for access and equity in our classrooms, The Journal of Applied Instructional Design, 10(1).
  23. Ramezan, C.A., T.A. Warner, A.E. Maxwell, and B.S. Price, 2021. Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data, Remote Sensing, 13(3): 368. https://doi.org/10.3390/rs13030368.
  24. Maxwell, A.E., M.S. Bester, L.A. Guillen, C.A. Ramezan, D.J. Carpinello, Y. Fan, F.M. Hartley, S.M. Maynard, and J.L. 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.
  25. Maxwell, A.E., and T.A. Warner, 2020. Thematic classification accuracy assessment with inherently uncertain boundaries: an argument for center-weighted accuracy assessment metrics, Remote Sensing, 12(12): 1905. https://doi.org/10.3390/rs12121905.
  26. Fang, F., B.E. McNeil, T.A. Warner, A.E. Maxwell, G.A. Dahle, E. Eutsler, and J. Li. 2020. Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington D.C., USA, Remote Sensing of Environment, 246: 111811. https://doi.org/10.1016/j.rse.2020.111811.
  27. Maxwell, A.E., P. Pourmohammadi, and J. Poyner, 2020. Mapping the topographic features of mining-related valley fills using mask R-CNN deep learning and digital elevation data, Remote Sensing, 12(3): 547. https://doi.org/10.3390/rs12030547.
  28. Maxwell, A.E., M. Sharma, J.S. Kite, K.A. Donaldson, J.A. Thompson, M.L. Bell, and S.M. Maynard, 2020. Slope failure prediction using random forest machine learning and LiDAR in an eroded folded mountain belt, Remote Sensing, 12(3): 486. https://doi.org/10.3390/rs12030486.
  29. Maxwell, A.E., M.P. Strager, T.A. Warner, C.A. Ramezan, A.N. Morgan, and C.E. Pauley, 2019. Large-area, high spatial resolution land cover mapping using random forests, GEOBIA, and NAIP orthophotography: findings and recommendations, Remote Sensing, 11(12): 1409. https://doi.org/10.3390/rs11121409.
  30. Ramezan, C.A., T.A. Warner, and A.E. Maxwell, 2019. Evaluation of sampling and cross-validation tuning strategies for regional-scale machine learning classification, Remote Sensing, 11(2): 185. https://doi.org/10.3390/rs11020185.
  31. Maxwell, A.E., and T.A. Warner, 2019. Is high spatial resolution DEM data necessary for mapping palustrine wetlands?, International Journal of Remote Sensing, 40(1): 118-137. https://doi.org/10.1080/01431161.2018.1506184.
  32. Fang, F., McNeil, B.E., Warner, T.A., and A.E. Maxwell, 2018. Combining high spatial resolution multi-temporal satellite data with leaf-on LiDAR to enhance tree species discrimination at the crown-level, International Journal of Remote Sensing, 39(23): 9054-9072. https://doi.org/10.1080/01431161.2018.1504343.
  33. Maxwell, A.E., T.A. Warner, and F. Fang, 2018. Implementation of machine learning classification in remote sensing: an applied review, International Journal of Remote Sensing, 39(9): 2784-2817. https://doi.org/10.1080/01431161.2018.1433343.
  34. Liebermann, H., J. Schuler, M.P. Strager, and A. Maxwell, 2018. A work flow and evaluation of using unmanned aerial systems for deriving forest stand characteristics in mixed hardwoods of West Virginia, Geospatial Applications in Natural Resources, 2(1): 23-53.
  35. Maxwell, A.E., T.A. Warner, B.C. Vanderbilt, and C.A. Ramezan, 2017. Land cover classification and feature extraction from National Agriculture Imagery Program (NAIP) orthoimagery: A Review, Photogrammetric Engineering & Remote Sensing, 83(11): 737-747. https://doi.org/10.14358/PERS.83.10.737.
  36. Strager, M.S., M. Thomas-Van Gundy, A.E. Maxwell, 2016. Predicting post-fire change in the Central Appalachians from remotely-sensed data, Geospatial Applications in Natural Resources, 1(2): 1-17.
  37. Maxwell, A.E., T.A. Warner, and M.P. Strager, 2016. Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables, Photogrammetric Engineering & Remote Sensing, 82(6): 437-447. https://doi.org/10.14358/PERS.82.6.437.
  38. Maxwell, A.E., and T.A. Warner, 2015. Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification, International Journal of Remote Sensing, 36(17): 4384-4410. https://doi.org/10.1080/01431161.2015.1083632.
  39. Maxwell, A.E., T.A. Warner, M.P. Strager, J.F. Conley, and A.L. Sharp, 2015. Assessing machine learning algorithms and image- and LiDAR-derived variables for GEOBIA classification of mining and mine reclamation, International Journal of Remote Sensing, 36(4): 954-978. https://doi.org/10.1080/01431161.2014.1001086.
  40. Merriam, E.R., J.T. Petty, M.P. Strager, A.E. Maxwell, and P.F. Ziemkiewicz, 2015. Complex contaminant mixtures in multi-stressor Appalachian riverscapes, Environmental Toxicology and Chemistry, 34(11): 2603-2610.
  41. Merriam, E.R., J.T. Petty, M.P. Strager, A.E. Maxwell, and P.F. Ziemkiewicz, 2015. Landscape-based cumulative effects models for predicting stream response to mountaintop mining in multi-stressor Appalachian watersheds, Freshwater Science, 34(3): 1006-1019.
  42. Strager, M.P., J.M. Strager, J.S. Evans, J.K. Dunscomb, B.J. Kreps, and A.E. Maxwell, 2015. Combining a spatial model and demand forecasts to map future surface coal mining in Appalachia, PLoS ONE, 10(6): e0128813.10.1371/journal.pone.0128813.
  43. Maxwell, A.E., M.P. Strager, T.A. Warner, N.P. Zégre, and C.B. Yuill, 2014. Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation, GIScience & Remote Sensing, 51(3): 301-320. https://doi.org/10.1080/15481603.2014.912874.
  44. Zegre, N., A. Miller, A. Maxwell, and S. Lamont, 2014. Multi-scale analysis of hydrology in a mountaintop mine-impacted watershed, Journal of the American Water Resources Association, doi: 10.1111/jawr.12184.
  45. Maxwell, A.E., T.A. Warner, M.P. Strager, and M. Pal, 2014. Combining RapidEye satellite imagery and LiDAR for mapping of mining and mine reclamation, Photogrammetric Engineering & Remote Sensing, 80(2): 179-189. https://doi.org/10.14358/PERS.80.2.179-189.
  46. Pal, M., A.E. Maxwell, and T.A. Warner, 2013. Kernel-based extreme learning machine for remote-sensing image classification, Remote Sensing Letters, 4(9): 853-862. https://doi.org/10.1080/2150704X.2013.805279.
  47. Merriam, E.R., J.T. Petty, M.P. Strager, A.E. Maxwell, and P.F. Ziemkiewicz, 2013. Scenario analysis predicts context-dependent stream response to landuse change in a heavily mined central Appalachian watershed, Freshwater Science, 32(4): 1246-1259.
  48. Zegre, N., A. Maxwell, and S. Lamont, 2013. Characterizing streamflow response of a mountaintop-mined watershed to changing land use, Applied Geography, 39: 5-15.
  49. Maxwell, A.E., and M.P. Strager, 2013. Assessing landform alterations induced by mountaintop mining, Natural Science, 5(2A): 52A034. http://dx.doi.org/10.4236/ns.2013.52A034.
  50. Maxwell, A.E., M.P. Strager, C.B. Yuill, and J.T. Petty, 2012. Modeling critical forest habitat in the Southern Coal Fields of West Virginia, International Journal of Ecology, Volume 2012, Article ID 182683, 10 pages.