Forest fuel load estimation with terrestrial LiDAR and machine learning regression
Deep learning semantic segmentation applied to historic topographic maps
Accuracy assessment using uncertain feature boundaries
Forest-type mapping using field plots, terrain derivatives, and multi-temporal, multispectral data
Mask R-CNN deep learning applied to digital terrain data
Large area, high spatial resolution predictive modeling of landslide occurrence
Assessing spatial patterns and risk factors associated with private college financial health
Development of free and open-source course materials
Development of WV Elevation and LiDAR Download Tool
Statewide, high spatial resolution land cover mapping using NAIP orthophotography and machine learning
Citizen science, remote sensing, and mapping quality of life: a West Virginia View citizen science initiative
Predicting topographic likelihood of palustrine wetland occurrence
Datasets and Tools
Click here to download the 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 ".
Click here to download the data associated with "Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps". The associated code can be found on GitHub here.
Click here to download the data associated with "Mapping the Topographic Features of Mining-Related Valley Fills using Mask R-CNN Deep Learning and Digital Elevation Data". An explanation of the data can be found here.
Click here to download the data associated with our GitHub slope failure probabilistic occurence mapping repo, which provides example code in Python and R.
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): 1-25. https://doi.org/10.3390/rs12244145.
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): 1-21. https://doi.org/10.3390/rs12121905.
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.
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): 1-27. https://doi.org/10.3390/rs12030486.
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: 1-27. https://doi.org/10.3390/rs11121409.
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 1-21. https://doi.org/10.3390/rs11020185.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.