DFPENet-geology claims new state-of-the-art landslide recognition accuracy using RGB imagery, geological morphology filters, and temporal subtraction, but the evaluation is undermined by training/test overlap and an excluded boundary error.
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DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides
DFPENet-geology claims new state-of-the-art landslide recognition accuracy using RGB imagery, geological morphology filters, and temporal subtraction, but the evaluation is undermined by training/test overlap and an excluded boundary error.