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.

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Land development processes are driven by complex interactions between socio-economic and spatial factors. Acquiring an understanding of such processes and the underlying procedures helps urban and regional planners, environmental scientists, and policy makers to base their decisions on valid and profound information. In this work, remote-sensing-derived land-cover data were used to characterize the patterns of land development from the beginning of 1985 to the beginning of 2015, in the state of West Virginia (WV), US. We applied spatial pattern analysis, ridge regression, and Geographically Weighted Ridge Regression (GWRR) to examine the impact of population, energy resources, existing land developments dynamics, and economic status on land transformation. We showed that in presence of multicollinearity of explanatory variables, how penalizing regression models in both local and global levels lead to a better fit and decreases the model’s variance. We used geographical error analysis of regression models to visualize the difference between the model estimates and actual values. The findings of this research indicate that because of shifting geography of opportunities, the patterns and processes of land development in the studied region are unstable. This leads to fragmented land developments and prevents formation of large communities.