Aaron E. Maxwell
Orcid: 0000-0002-4412-5599
According to our database1,
Aaron E. Maxwell
authored at least 16 papers
between 2019 and 2024.
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Collaborative distances:
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Bibliography
2024
Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices.
Remote. Sens., February, 2024
2023
Synthetic Forest Stands and Point Clouds for Model Selection and Feature Space Comparison.
Remote. Sens., September, 2023
Comparing harmonic regression and GLAD Phenology metrics for estimation of forest community types and aboveground live biomass within forest inventory and analysis plots.
Int. J. Appl. Earth Obs. Geoinformation, August, 2023
2022
Enhancing Reproducibility and Replicability in Remote Sensing Deep Learning Research and Practice.
Remote. Sens., 2022
2021
Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data.
Remote. Sens., 2021
Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies - Part 2: Recommendations and Best Practices.
Remote. Sens., 2021
Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies - Part 1: Literature Review.
Remote. Sens., 2021
Remote. Sens., 2021
Remote. Sens., 2021
Assessing the Generalization of Machine Learning-Based Slope Failure Prediction to New Geographic Extents.
ISPRS Int. J. Geo Inf., 2021
2020
Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics.
Remote. Sens., 2020
Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt.
Remote. Sens., 2020
Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data.
Remote. Sens., 2020
Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps.
Remote. Sens., 2020
2019
Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification.
Remote. Sens., 2019
Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations.
Remote. Sens., 2019