Landslide detection and characterization using terrestrial 3D laser scanning (LiDAR)

Ozdogan, Mehmet Volkan

doi:10.13168/agg.2019.0032

Cited by 9 publications

(7 citation statements)

References 49 publications

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“…At various costs, cameras can be installed on terrestrial platforms, including vehicles, tripods, and handheld devices such as mobile phones [28,42,44,76,[82][83][84]. Terrestrial platforms allow capturing images in real-time at close range, thus gaining a clear and detailed view of a landslide area [28,44].…”

Section: Terrestrial Sensorsmentioning

confidence: 99%

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Albanwan,

Qin,

Liu

2024

Remote Sensing

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Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications.

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Section: Terrestrial Sensorsmentioning

confidence: 99%

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Albanwan,

Qin,

Liu

2024

Remote Sensing

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“…M3C2 change results can be used to identify statistically significant changes by estimating a "Level of Detection" [36], which is often underestimated on natural surfaces and, thereby, causes false negatives [35,44,49,50]. Winiwarter et al [41] supplemented the M3C2 calculation with error propagation and covariance information.…”

Section: Traditional Deformation Analysis and Change Detectionmentioning

confidence: 99%

Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

et al. 2023

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Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces.

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“…It also does not need to touch the landslide body [42]. Consequently, numerous studies these days focus on using TLS for monitoring landslides [43][44][45][46][47][48]. However, this method also has some disadvantages, including a high volume of generated point clouds, especially for large areas, and the problem of accurately registering these point clouds.…”

Section: Terrestrial Laser Scannersmentioning

confidence: 99%

3D Landslide Monitoring in High Spatial Resolution by Feature Tracking and Histogram Analyses Using Laser Scanners

Hosseini,

Reindl,

Raffl

et al. 2023

Remote Sensing

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Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques.

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Landslide detection and characterization using terrestrial 3D laser scanning (LiDAR)

Cited by 9 publications

References 49 publications

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

3D Landslide Monitoring in High Spatial Resolution by Feature Tracking and Histogram Analyses Using Laser Scanners

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