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

文献类型：期刊论文

作者Liu, Changqing1,2; Bao, Han1; Zhang, Jingfeng1; Lan, Hengxing3; Adriano, Bruno2; Koshimura, Shunichi2; Yuan, Wei2
刊名REMOTE SENSING
出版日期2025-10-28
卷号17期号:21页码:3555
关键词remote sensing technologies digital reconstruction rock slope slope monitoring point cloud registration deep learning
DOI10.3390/rs17213555
产权排序3
文献子类Article
英文摘要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.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.Abstract 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.
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WOS关键词REGISTRATION
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001613120600001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/217684]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Bao, Han
作者单位1.Changan Univ, Sch Highway, Xian 710064, Peoples R China;
2.Tohoku Univ, Int Res Inst Disaster Sci, Sendai 9808572, Japan;
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Changqing,Bao, Han,Zhang, Jingfeng,et al. Accurate Digital Reconstruction of High-Steep Rock Slope via Transformer-Based Multi-Sensor Data Fusion[J]. REMOTE SENSING,2025,17(21):3555.
APA Liu, Changqing.,Bao, Han.,Zhang, Jingfeng.,Lan, Hengxing.,Adriano, Bruno.,...&Yuan, Wei.(2025).Accurate Digital Reconstruction of High-Steep Rock Slope via Transformer-Based Multi-Sensor Data Fusion.REMOTE SENSING,17(21),3555.
MLA Liu, Changqing,et al."Accurate Digital Reconstruction of High-Steep Rock Slope via Transformer-Based Multi-Sensor Data Fusion".REMOTE SENSING 17.21(2025):3555.

入库方式： OAI收割

来源：地理科学与资源研究所

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