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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2019 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.