Harnessing Remote Sensing for Civil Engineering: Then, Now, and Tomorrow

Laefer, Debra F.

doi:10.1007/978-981-13-7067-0_1

Cited by 11 publications

(8 citation statements)

References 67 publications

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“…To date, most studies use laser scanning point clouds to identify surface deficiencies (e.g. water leakage or Structural assessment moisture), determine material loss (area and volume losses) and deformation measurement, which are investigated in this section, while other applications of the point cloud can refer to Laefer (2020).…”

Section: Related Workmentioning

confidence: 99%

Structural assessment using terrestrial laser scanning point clouds

Truong-Hong

Lindenbergh

Nguyen

2021

IJBPA

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PurposeTerrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.Design/methodology/approachIn practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface Sref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.FindingsThe study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.Research limitations/implicationsDue to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.Practical implicationsThis study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.Social implicationsThe results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.Originality/valueAlthough a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

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Section: Related Workmentioning

confidence: 99%

Structural assessment using terrestrial laser scanning point clouds

Truong-Hong

Lindenbergh

Nguyen

2021

IJBPA

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“…В некоторых случаях применение только NDVI не может дать корректную оценку полученных данных со снимков. Например, при достижении определенного порога развития растения индекс теряет чувствительность, то есть если растение развивается очень активно, то по NDVI нельзя отличить аномально зеленое растение от «обычного» зеленого [22].…”

Section: рис 2 корреляция между показателем Ndvi и продуктивностью дл...unclassified

Studying the vegetation impact of terrestrial ecosystems on reducing the carbon footprint in in the territory of the Russian Federation

Пашкевич

Korotaeva

2021

Vestn. Ross. univ. družby nar., Ser. Èkol. bezop. žiznedeât.

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Plant communities of terrestrial ecosystems of the Russian Federation are studied in terms of their ability to reduce the carbon footprint as a result of carbon dioxide sequestration. The classification of typical plant communities and the division of the territory depending on the climatic and regional characteristics is given, with further provision of values of the specific absorption capacity of growing plant communities according to the division presented. To assess the biomass of vegetation, as well as its dynamics of change, an analysis of the remote sensing method was carried out as the most preferred method for determining biomass in real time. The characteristics of currently used remote sensing systems, including IKONOS, Quickbird, Worldview, ZY-3, SPOT, Sentinel, Landsat and MODIS are given. The main indicators used for the indexation assessment of vegetation biomass are listed, with subsequent prediction based on them of the efficiency of carbon dioxide uptake by plant communities.

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“…LiDAR utilizes laser scanning equipment, global positioning systems (GPS), and navigation technologies to obtain intensity and positional information of surrounding features. The output of the LiDAR scanning process is a rich 3D point cloud of the surveyed objects that allows the extraction and assessment of road and roadside features and can support creating an accurate representation of the environment from the collected data (Gargoum & El‐Basyouny, 2019; Gouda, El‐Basyouny, & Epp, 2020; Hinks, Carr, Gharibi, & Laefer, 2015; Laefer, 2020; Vo, Laefer, Smolic, & Zolanvari, 2019; Zhang, Vo, Laefer, & Porfiri, 2019). Due to its millimeter‐level accuracy, cost saving in data collection, and widespread data collection capabilities, civil engineering research has gravitated toward the use of point cloud data (Laefer, 2020; H. S. Park, Lee, Adeli, & Lee, 2007; S. W. Park, Park, Kim, & Adeli, 2015).…”

Section: Introductionmentioning

confidence: 99%

Octree‐based point cloud simulation to assess the readiness of highway infrastructure for autonomous vehicles

Gouda

Mirza

Weiß

et al. 2021

Computer aided Civil Eng

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Autonomous vehicles (AVs) are anticipated to supersede human drivers with an expectation of improved safety and operation. Since current infrastructure is designed based on the constraints caused by human drivers, it must be reassessed for autonomous driving compatibility. Recently, representatives from the infrastructure owners/operators (IOOs), automotive industry, and academia have advocated for new approaches to prepare roadways for the deployment of AVs over the next decade. Following these recommendations, this paper proposes a novel, simulation‐based approach for the assessment of highways "readiness" for AVs using 3D point cloud data. The proposed method uses octrees to perform volumetric queries for potential obstructions within an AV sensory field. The proposed approach is compared to a state‐of‐the‐art raycasting approach. Consequently, available sight distances and maximum safe speed limits based on road and AV characteristics are proposed. Finally, a discussion of the potential mitigation measures at the locations with limited sight distances is presented.

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Harnessing Remote Sensing for Civil Engineering: Then, Now, and Tomorrow

Cited by 11 publications

References 67 publications

Structural assessment using terrestrial laser scanning point clouds

Structural assessment using terrestrial laser scanning point clouds

Studying the vegetation impact of terrestrial ecosystems on reducing the carbon footprint in in the territory of the Russian Federation

Octree‐based point cloud simulation to assess the readiness of highway infrastructure for autonomous vehicles

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