Accurate Digital Reconstruction of High-Steep Rock Slope via Transformer-Based Multi-Sensor Data Fusion

Abstract

Highlights: What are the main findings? Partial overlap, large outliers, and density heterogeneity in TLS–UAV data were revealed. A Transformer-based fusion method was introduced for high-steep slope reconstruction. What are the implications of the main findings? Accurate digital modeling of complex mountainous terrains was enabled. Reliable data support for disaster warning and risk mitigation was provided. Accurate and comprehensive characterization of high-steep slopes is crucial for real-time risk prediction, disaster assessment, and damage evolution monitoring. The study focused on a high-steep rocky slope along the Yanjiang Expressway in Sichuan Province, China. A novel digital reconstruction method was introduced, which integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry through a Transformer-based method combining GeoTransformer with the Maximal Cliques (MAC) algorithm. The results indicated that TLS excels in capturing fine-scale features, whereas UAV demonstrates superior performance in large-scale terrain reconstruction. However, multi-sensor data exhibit heterogeneity in terms of partial overlap, large outliers, and density differences. To address these challenges, the GeoTransformer-MAC framework extracts geometrically invariant features from cross-source point cloud (CSPC) to establish initial correspondences, followed by rigorous screening of high-quality locally consistent correspondences to optimize transformation parameters. This method achieves accurate digital reconstruction of the high-steep rock slope. Global and local error analyses verify the model’s superiority in both overall slope characterization and fine-scale feature representation. Compared with the TLS-only model and the conventional method, the Transformer-based method improves the slope model integrity by 85.58%, increases the data density by 9.71%, and improves the accuracy by nearly threefold. This study provides a novel approach for the digital modeling of complex terrains, which serves the refined identification and modeling of geohazards for high-steep slopes in complex mountainous regions.