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Dense 3D displacement estimation for landslide monitoring via fusion of TLS point clouds and embedded RGB images

Year: 2026
Type: article
Abstract: Landslide monitoring is essential for understanding geohazards and mitigating associated risks. Existing point cloud-based methods, however, typically rely on either geometric or radiometric information and often yield sparse or non-3D displacement estimates. In this paper, we propose a hierarchical partitioning-based coarse-to-fine approach that fuses 3D point clouds and co-registered RGB images to estimate dense 3D displacement vector fields. Patch-level matches are constructed using both 3D geometry and 2D image features, refined via geometric consistency checks, and followed by rigid transformation estimation per match. Experimental results on two real-world landslide datasets demonstrate that the proposed method produces 3D displacement estimates with high spatial coverage (79% and 97%) and accuracy. Deviations in displacement magnitude with respect to external measurements (total station or GNSS observations) are 0.15 m and 0.25 m on the two datasets, respectively, and only 0.07 m and 0.20 m compared to manually derived references, all below the mean scan resolutions (0.08 m and 0.30 m). Compared with the state-of-the-art method F2S3, the proposed approach improves spatial coverage while maintaining comparable accuracy. The proposed approach offers a practical and adaptable solution for TLS-based landslide monitoring and is extensible to other types of point clouds and monitoring tasks. • Hierarchical partitioning-based coarse-to-fine 3D displacement estimation for TLS-based landslide monitoring. • Fusion of TLS point clouds and embedded RGB images. • High accuracy and high spatial coverage demonstrated on real-world landslide datasets.
DOI: 10.1016/j.jag.2026.105093
Language: English
Open Access status: gold
Estimated APC paid: $3,550
OpenAlex ID: W4417538024
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Cites: 61
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