Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds

Bakuła, K.; Pilarska, M.; Salach, Adam; Kurczyński, Z.

doi:10.3390/ijgi9040248

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…From the remaining point cloud, the two types of data were analyzed, and defects were identified. A methodology for monitoring and detecting defects in levees was developed by [99], based on multi-temporal LiDAR point clouds and RGB images. The LiDAR data was used to produce two differential Digital Terrain Models, and the elevation changes were identified.…”

Section: Retaining Wallsmentioning

confidence: 99%

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Kaartinen

Dunphy

Sadhu

2022

Sensors

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As innovative technologies emerge, extensive research has been undertaken to develop new structural health monitoring procedures. The current methods, involving on-site visual inspections, have proven to be costly, time-consuming, labor-intensive, and highly subjective for assessing the safety and integrity of civil infrastructures. Mobile and stationary LiDAR (Light Detection and Ranging) devices have significant potential for damage detection, as the scans provide detailed geometric information about the structures being evaluated. This paper reviews the recent developments for LiDAR-based structural health monitoring, in particular, for detecting cracks, deformation, defects, or changes to structures over time. In this regard, mobile laser scanning (MLS) and terrestrial laser scanning (TLS), specific to structural health monitoring, were reviewed for a wide range of civil infrastructure systems, including bridges, roads and pavements, tunnels and arch structures, post-disaster reconnaissance, historical and heritage structures, roofs, and retaining walls. Finally, the existing limitations and future research directions of LiDAR technology for structural health monitoring are discussed in detail.

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Section: Retaining Wallsmentioning

confidence: 99%

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Kaartinen

Dunphy

Sadhu

2022

Sensors

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“…These outliers are marked as invalid and excluded from further analysis. The remaining points are clustered according to their Euclidean distance to separate the spalling far from the ideal cylinder from the giant cluster that corresponds to the intact surface [38].…”

Section: ) Cross Section Extractionmentioning

confidence: 99%

Defect Detection for a Vertical Shaft Surface Based on Multimodal Sensors

Chu

Tang

Sun

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hydroelectricity is a major source of renewable electricity originating from a turbine driven by dammed largevolume water via a penstock or a drop shaft. Shafts suffer from risks of collapse due to the pressure from exterior structures and the erosion from inner water flow. Vertical shafts are an important part of hydroelectric power generation systems, and detecting defects in shafts guarantees the stable operation of hydropower stations. However, shaft defect detection is a great challenge due to the poor conditions, large drop in height, limited entrance size, lack of light and damp air, where suitable technology and equipment are not available. Aiming at defect detection for vertical shafts, we have developed a defect-detecting system based on unmanned airships, integrated panoramic CCD cameras, 3D laser scanners, inertial measurement units, barometric altimeters, illumination sensors, and control modules. Shaft defect detection methods (SDDMs) are proposed by fusing the multi-modal image features to extract typical defects on concrete surfaces. Compared with machine learning methods, the proposed method achieves the highest overall accuracy of 90.90% for defect detection. Our system was validated by experiments in the shafts of the Nuozhadu hydropower station to be functional for defect detection, which demonstrates its capability of reducing the risk of collapse and improving safety.

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“…The software is one of the most popular programs used in photogrammetry (mainly in short-range photogrammetry) [53]. Aside from standard functionalities (photogrammetric triangulation [27], digital surface/digital terrain model generation [54,55], georeferenced orthomosaic generation [56,57], 3D model generation and texturing [58]), it enables the use of Python and Java APIs, which allow for adapting the software to the user's needs, often dictated by its use for specialist analyses by scientists in the photogrammetry and teledetection fields [23,59,60].…”

Section: Data Acquisitionmentioning

confidence: 99%

Seven Different Lighting Conditions in Photogrammetric Studies of a 3D Urban Mock-Up

et al. 2021

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One of the most important elements during photogrammetric studies is the appropriate lighting of the object or area under investigation. Nevertheless, the concept of “adequate lighting” is relative. Therefore, we have attempted, based on experimental proof of concept (technology readiness level—TRL3), to verify the impact of various types of lighting emitted by LED light sources for scene illumination and their direct influence on the quality of the photogrammetric study of a 3D urban mock-up. An important issue in this study was the measurement and evaluation of the artificial light sources used, based on illuminance (E), correlated colour temperature (CCT), colour rendering index (CRI) and Spectral power distribution (SPD) and the evaluation of the obtained point clouds (seven photogrammetric products of the same object, developed for seven different lighting conditions). The general values of the quality of the photogrammetric studies were compared. Additionally, we determined seventeen features concerning the group of tie-points in the vicinity of each F-point and the type of study. The acquired traits were related to the number of tie-points in the vicinity, their luminosities and spectral characteristics for each of the colours (red, green, blue). The dependencies between the identified features and the obtained XYZ total error were verified, and the possibility of detecting F-points depending on their luminosity was also analysed. The obtained results can be important in the process of developing a photogrammetric method of urban lighting monitoring or in selecting additional lighting for objects that are the subject of a short-range photogrammetric study.

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Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds

Cited by 12 publications

References 22 publications

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems

Defect Detection for a Vertical Shaft Surface Based on Multimodal Sensors

Seven Different Lighting Conditions in Photogrammetric Studies of a 3D Urban Mock-Up

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