Multi-temporal analysis of mountainous terrain changes based on UAV images and point cloud data.

Tsung-Chin Hou, Thanh Bao Nhut Trinh, Tzu-Yi Yang, Jia-Ying Lu, Bao-Tian Chiang, Shun-Hong Zhou, Jiun-Shyan Chen

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

7 authors.

Tsung-Chin HouDepartment of Civil Engineering, National Cheng Kung University, Tainan, Taiwan. tchou@mail.ncku.edu.tw.

Thanh Bao Nhut TrinhDepartment of Civil Engineering, National Cheng Kung University, Tainan, Taiwan.

Tzu-Yi YangDepartment of Civil Engineering, National Cheng Kung University, Tainan, Taiwan.

Jia-Ying LuDepartment of Civil Engineering, National Cheng Kung University, Tainan, Taiwan.

Bao-Tian ChiangDepartment of Civil Engineering, National Cheng Kung University, Tainan, Taiwan.

Shun-Hong ZhouDepartment of Civil Engineering, National Cheng Kung University, Tainan, Taiwan.

Jiun-Shyan ChenDepartment of Structural Engineering, University of California San Diego, San Diego, USA.

Funding

National Science and Technology Council, Taiwan NSTC 113-2625-M-006-002Yushan Fellow Program, Ministry of Education, Taiwan MOE 113-YSFEE-0005-001-P1

6 · The paper itself

Abstract

Accurate multi-temporal terrain change detection in mountainous regions is crucial for disaster monitoring and environmental management; however, it remains challenging due to the complex topography, dense vegetation, and occlusion. This study proposes an integrated, automated framework for terrain change detection that combines 2D semantic segmentation and 3D geometric analysis, utilizing UAV imagery and point cloud data. This method uses the DeepLabV3 neural network to perform multi-class semantic segmentation of orthophotos, combining Fast Point Feature Histograms (FPFH) and Random Sample Consensus (RANSAC) algorithms, followed by Iterative Closest Point (ICP) based local refinement. Geometric changes are subsequently quantified using the Multiscale Model-to-Model Cloud Comparison (M3C2) distance metric. When applied to 0.06 km

Indexed as

Deep learningMulti-temporal analysisPoint cloud dataTerrain change detectionUAV images

Identifiers

PMID41803244

DOI10.1038/s41598-026-42202-x

PMCPMC13096470

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.