Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

Xiao, Huai‐xian; Jiang, Nan; Chen, Xing‐zhen; Hao, Ming‐hui; Zhou, Jia‐wen

doi:10.1002/gj.4677

Cited by 4 publications

(4 citation statements)

References 46 publications

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“…By comparing consecutive images, SPOT can recognize these small changes and convert them into displacement vectors (Giles et al, 2009). The core of the method is to set a search window on the slave image (post-event image) and a search range larger than this search window on the master image (pre-event image), and then use normalized cross correlation (NCC) to match the slave image and the master image and calculate a correlation coefficient surface, with the peak position of the surface and the vector connecting the center of the search window of the master image to represent the displacement of the ground surface, and then finally through the interpolation to achieve the output of the displacement field of the entire images (Xiao et al, 2023).…”

Section: Landslide Displacement Vector Calculationmentioning

confidence: 99%

“…At present, commonly used methods for landslide monitoring in reservoir areas include contact monitoring such as Global Navigation Satellite System (GNSS) and inclinometers (Chen et al, 2017;Liang et al, 2021), and remote sensing techniques such as satellite imagery (Chen et al, 2015), interferometric synthetic aperture radar (InSAR) (Zhao et al, 2018;Zhou et al, 2020;Ghorbanzadeh et al, 2022), unmanned aerial vehicle (UAV) photogrammetry (Xu et al, 2018;Li et al, 2019a;Xiao et al, 2023), and light detection and ranging (LiDAR) (Li et al, 2019b;Jiang et al, 2020a;Booth et al, 2020;Zhou et al, 2023). Each of these methods has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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Reservoir landslide monitoring and mechanism analysis based on UAV photogrammetry and sub-pixel offset tracking: a case study of Wulipo landslide

Li,

Jiang,

Shi

et al. 2024

Front. Earth Sci.

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Introduction: Reservoir landslides undergo large deformations during the early stages of impoundment and maintain long-term persistent deformations during the operational period of the reservoir. The management of reservoir landslides mostly focuses on the early identification, risk assessment during the large deformations, and long-sequence monitoring during long-term persistent deformations, which requires sufficient continuity and integrity of the landslide monitoring data.Methods: Taking the Wulipo (WLP) landslide in Baihetan Reservoir as example, this paper proposes a reservoir landslide monitoring method that integrates field survey, unmanned aerial vehicle (UAV) photogrammetry and global navigation satellite system (GNSS) monitoring, which can effectively eliminate the practical monitoring gaps between multiple monitoring methods and improve the continuity and completeness of monitoring data.Results and discussion: First, this study determined the initiation time of the landslide through the field investigation and collected five period of UAV data to analyze the overall displacement vector of the WLP landslide using sub-pixel offset tracking (SPOT). On the basis of the above data, we compensated for the missing data in GNSS system due to the practical monitoring vacancies by combining the field survey and the landslide-water level relationship. Based on these monitoring data, this paper points out that the WLP landslide is a buoyancy-driven landslide, and whether or not accelerated deformation will occur is related to the maximum reservoir water level. Finally, this study analyzed and discussed the applicability of UAV photogrammetry for reservoir landslide monitoring in the absence of ground control points (GCPs), and concluded that this method can be quickly and flexibly applied to the stage of large deformation of reservoir landslides.

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Section: Landslide Displacement Vector Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reservoir landslide monitoring and mechanism analysis based on UAV photogrammetry and sub-pixel offset tracking: a case study of Wulipo landslide

Li,

Jiang,

Shi

et al. 2024

Front. Earth Sci.

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“…Detecting slope deformations is crucial in engineering to ensure the safety of projects and personnel by avoiding catastrophic slope failures. Xiao et al (2023) propose an integrated approach that combines subpixel offset tracking (sPOT) and unsupervised change detection algorithms using unmanned aerial vehicle (UAV) image datasets for detecting deformations in a quarry slope. The sPOT algorithm provides a surface displacement field of the slope with subpixel accuracy, while the unsupervised change detection algorithm yields the ground object reconstruction area of the slope to verify and explain the sPOT results.…”

Section: Research Outputs Of This Special Issuementioning

confidence: 99%

Application of artificial intelligence in geotechnical and geohazard investigations

et al. 2023

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The application of artificial intelligence (AI) and big data in geohazard investigations has gained popularity due to the development of machine learning algorithms and data collection methods. Previous studies have compared and applied various machine learning‐based methods, such as conventional machine learning, deep learning, and transfer learning in different areas. This special issue provides state‐of‐the‐art information on the use of AI in geotechnical research, particularly in the Three Gorges Reservoir (TGR) area and adjoining regions. The aim of this volume is to serve as a reference for future researchers interested in exploring the potential of AI in geohazard investigations. It is hoped that this special issue will contribute to the development of guidelines for enhancing the application of AI and big data in geotechnical research, thereby improving our understanding of geological terrains and their associated hazards.

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“…On the other hand, aerial surveying has gained popularity as a fast and safe solution within outdoor missions. In recent years, drone photogrammetry has become a common solution for surveying stockpiles in a variety of industries such as mining [4,5], quarrying [6,7], construction site monitoring [8,9], and agriculture and forestry [10,11]. In fact, large volumes of aerial 2D photos are typically processed using classical photogrammetry methods or structure from motion (SfM; a more modern approach that automates much of the processes involved in classical photogrammetry) [12,13] to create 3D topographical models and orthomosaic maps.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Stockpile Volume Estimation in Open and Confined Environments: A Review

Alsayed,

Nabawy

2023

Drones

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This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces.

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Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

Cited by 4 publications

References 46 publications

Reservoir landslide monitoring and mechanism analysis based on UAV photogrammetry and sub-pixel offset tracking: a case study of Wulipo landslide

Reservoir landslide monitoring and mechanism analysis based on UAV photogrammetry and sub-pixel offset tracking: a case study of Wulipo landslide

Application of artificial intelligence in geotechnical and geohazard investigations

Stockpile Volume Estimation in Open and Confined Environments: A Review

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