GTSNet: A Global Topography-Aware Segmentation Network for Remote Sensing Identification of Unstable Rock Masses
TL;DR
A dual-modality deep learning network that integrates high-resolution unmanned aerial vehicle (UAV) imagery and digital elevation model (DEM) data and introduces DEM-derived terrain-semantic information into optical feature modeling through a multi-scale Topography-aware Fusion Module is proposed.
Abstract
The high-precision identification of unstable rock masses in rugged terrain is important for engineering safety and geological hazard prevention. However, shadow occlusion, complex backgrounds, and blurred boundaries caused by rugged terrain often limit the performance of optical feature-based segmentation models, resulting in missed detections and false positives. In this study, to address this issue, we propose a dual-modality deep learning network, named the Global Topography-aware Segmentation Network (GTSNet), that integrates high-resolution unmanned aerial vehicle (UAV) imagery and digital elevation model (DEM) data. The proposed network introduces DEM-derived terrain-semantic information into optical feature modeling through a multi-scale Topography-aware Fusion Module. By using high-level geomorphological context to adaptively recalibrate low-level spatial details, GTSNet improves boundary representation and reduces interference from complex backgrounds. Experiments were conducted on one unstable rock mass dataset compiled from UAV data collected at seven alpine canyon hydropower engineering areas in China. The results show that GTSNet achieved an overall accuracy (Acc) of 90.14%, an F1-score of 75.75%, and an intersection over union (IoU) of 60.97%, showing higher segmentation performance than the six compared semantic segmentation networks under the same RGB + DEM input setting. In addition, GTSNet obtained a Precision of 74.87% and a Recall of 76.66%, indicating a more balanced performance between missed detections and false positives. The results suggest that the integration of RGB imagery and DEM-derived terrain information, together with global context modeling and topography-aware feature fusion, contributes to improved unstable rock mass segmentation in complex canyon environments. This study provides a useful deep learning framework for UAV-based unstable rock mass interpretation in hydropower engineering areas.