TL;DR
Results indicate that RSBG can effectively improve segmentation quality in complex landslide regions and shows strong potential for landslide remote sensing applications.
Results indicate that attention-based feature refinement and boundary-aware supervision can work complementarily to improve landslide segmentation while maintaining a favorable balance between segmentation performance and computational efficiency.
Two improved models enhanced the accuracy of landslide identification and resistance to interference, demonstrating potential for landslide monitoring and emergency response.
A deep learning-based semantic segmentation framework using the U-Net architecture for pixel-level landslide delineation from multispectral and topographical remote sensing data and establishes a foundation for further investigation of advanced Transformer- and attention-based architectures is presented.
Accurate landslide extraction plays an important role in landslide inventory mapping, geological hazard assessment, and risk management in mountainous areas. However, optical imagery alone often has difficulty distinguishing landslides from spectrally similar background objects, especially under vegetation cover and in...
This study showcases the power of deep learning technology in automating landslides’ detection processes and provides a basis for incorporating the technique into the disaster monitoring system framework.
Road extraction from high-resolution remote sensing images is crucial for urban planning and geographic information systems (GIS). However, complex background interference, severe occlusions, and the inherent morphological complexity of roads often lead to discontinuities and insufficient accuracy in extraction results...
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