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Open AccessArticle

Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data

1
Department of Geology and Geography, West Virginia University, Morgantown, WV 26506, USA
2
Davis College of Agriculture, Natural Resources, and Design, Department of Design and Community Development, West Virginia University, Morgantown, WV 26506, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 547; https://doi.org/10.3390/rs12030547
Received: 10 December 2019 / Revised: 22 January 2020 / Accepted: 5 February 2020 / Published: 7 February 2020
Modern elevation-determining remote sensing technologies such as light-detection and ranging (LiDAR) produce a wealth of topographic information that is increasingly being used in a wide range of disciplines, including archaeology and geomorphology. However, automated methods for mapping topographic features have remained a significant challenge. Deep learning (DL) mask regional-convolutional neural networks (Mask R-CNN), which provides context-based instance mapping, offers the potential to overcome many of the difficulties of previous approaches to topographic mapping. We therefore explore the application of Mask R-CNN to extract valley fill faces (VFFs), which are a product of mountaintop removal (MTR) coal mining in the Appalachian region of the eastern United States. LiDAR-derived slopeshades are provided as the only predictor variable in the model. Model generalization is evaluated by mapping multiple study sites outside the training data region. A range of assessment methods, including precision, recall, and F1 score, all based on VFF counts, as well as area- and a fuzzy area-based user’s and producer’s accuracy, indicate that the model was successful in mapping VFFs in new geographic regions, using elevation data derived from different LiDAR sensors. Precision, recall, and F1-score values were above 0.85 using VFF counts while user’s and producer’s accuracy were above 0.75 and 0.85 when using the area- and fuzzy area-based methods, respectively, when averaged across all study areas characterized with LiDAR data. Due to the limited availability of LiDAR data until relatively recently, we also assessed how well the model generalizes to terrain data created using photogrammetric methods that characterize past terrain conditions. Unfortunately, the model was not sufficiently general to allow successful mapping of VFFs using photogrammetrically-derived slopeshades, as all assessment metrics were lower than 0.60; however, this may partially be attributed to the quality of the photogrammetric data. The overall results suggest that the combination of Mask R-CNN and LiDAR has great potential for mapping anthropogenic and natural landscape features. To realize this vision, however, research on the mapping of other topographic features is needed, as well as the development of large topographic training datasets including a variety of features for calibrating and testing new methods. View Full-Text
Keywords: light detection and ranging; LiDAR; deep learning; convolutional neural networks; CNNs; mask regional-convolutional neural networks; mask R-CNN; digital terrain analysis; resource extraction light detection and ranging; LiDAR; deep learning; convolutional neural networks; CNNs; mask regional-convolutional neural networks; mask R-CNN; digital terrain analysis; resource extraction
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MDPI and ACS Style

Maxwell, A.E.; Pourmohammadi, P.; Poyner, J.D. Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data. Remote Sens. 2020, 12, 547.

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