A Robust and Efficient Method for Power Lines Extraction from Mobile LiDAR Point Clouds

Shokri, Danesh; Rastiveis, H.; Sarasua, Wayne; Shams, Alireza; Homayouni, Saeid

doi:10.1007/s41064-021-00155-y

Cited by 12 publications

(14 citation statements)

References 50 publications

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“…Because the maximum length between two adjacent poles in the United States is typically about 90 m (Wydra et al, 2018), the authors used a section length ( L ) equal to 90 m (Figure 3c). For more information on the sectioning process, the readers are referred to Shokri, Rastiveis, Sarasua, et al (2021).…”

Section: Methodsmentioning

confidence: 99%

“…Existing proposed methods for power line extraction from a mobile terrestrial laser scanning (MTLS) point cloud provide mixed results and are time consuming to implement. Some of these methods are based on Hough transform (HT) or random sample consensus (RANSAC) algorithms that perform well in ideal conditions but are sensitive to less‐than‐ideal conditions (Guan et al, 2016; Lehtomäki et al, 2019; Shokri, Rastiveis, Sarasua, et al, 2021; Yadav & Chousalkar, 2017). For example, challenging scenarios like power line cables partially occluded by tree branches and other objects, abrupt changes in the direction of power lines, and complex geometric characteristics of power line components make it clear that more research is needed in automated power line extraction to improve processing time and accuracy (Husain & Vaishya, 2018; Wang et al, 2019; Xu & Wang, 2019).…”

Section: Introductionmentioning

confidence: 99%

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An enhanced descriptor extraction algorithm for power line detection from point clouds

Shokri

Rastiveis

Sarasua

et al. 2023

Geographical Research

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Mobile terrestrial laser scanning (MTLS) systems provide a safe and efficient means to survey roadway corridors at high speed. MTLS point clouds are rich in planimetric data. However, manual extraction of useful information from these point clouds can be time consuming and laborious and automated object extraction from MTLS point clouds has become a hot topic in the remote sensing community. This study proposes an automated method for power line extraction from MTLS point clouds based on a multilayer perceptron (MLP) neural network. The proposed method consists of three main steps: (i) point cloud preprocessing, (ii) descriptor extraction and selection, and (iii) point classification. The preprocessing step involves filtering out more than 90% of the point cloud by eliminating the vast majority of unneeded points. Next, various descriptors are extracted from the remaining points including planarity, linearity, and verticality, and the descriptor standard deviation is used to select the best‐suited descriptors for power line extraction. Finally, an MLP neural network is trained using the selected descriptors from several cable and noncable sample points. The proposed algorithm was evaluated in three MTLS point clouds in urban and nonurban environments totalling 5.5 kilometres in length. An average precision of 94% and a recall of 94% showed the algorithm’s reliability and feasibility.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An enhanced descriptor extraction algorithm for power line detection from point clouds

Shokri

Rastiveis

Sarasua

et al. 2023

Geographical Research

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“…Close-up photogrammetry and terrestrial laser scanning (TLS) are frequently preferred techniques for creating 3D models of small to large objects, buildings, and historical and cultural artifacts [2][3][4]. TLS has been a popular measurement technique in recent years for documenting objects, figures, historical buildings, and cultural heritages [5][6][7]. Point data produced by high-resolution laser scanning offers various solutions in cases where conventional techniques are impractical or impossible to apply [6,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…TLS has been a popular measurement technique in recent years for documenting objects, figures, historical buildings, and cultural heritages [5][6][7]. Point data produced by high-resolution laser scanning offers various solutions in cases where conventional techniques are impractical or impossible to apply [6,[8][9][10]. Unmanned Aerial Systems (UASs) are cost-effective systems capable of low-altitude flight, which can be operated remotely due to their pilotless use [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment Toward Merging of Terrestrial Laser Scanner Point Data and Unmanned Aerial System Point Data

Karasaka

Makineci

ERDAL

2023

Konya Journal of Engineering Sciences

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Terrestrial Laser Scanning (TLS) techniques are widely preferred for 3D models of small and large objects, buildings, and historical and cultural heritages. However, sometimes relying on a single method for 3D modelling an object/structure is insufficient to arrive at a solution or meet expectations. For example, Unmanned Aerial Systems (UAS) provide perspective for building roofs, while terrestrial laser scanners provide general information about building facades. In this research, several facades of a selected building could not be modelled using terrestrial laser scanning, and UAS was used to complete the missing data for 3D modelling. The transformation matrix, a linear function, is created to merge different data types. In the transformation matrix, the scale was found to be 1:1.012. The accuracy analysis of the produced 3D model was also made by comparing the spatial measurements taken from different building facades and the differences in the measurement values obtained from the 3D model and calculating statistically. According to the accuracy analysis results, the Root Mean Square Error (RMSE) value is approximately 3 cm. The results of the accuracy research, which are within the 95% confidence interval with the three-sigma rule, are approximately 2 cm as RMSE. As a result of the study, it was determined that the data obtained from UAV photogrammetry and the data obtained by the TLS technique could be combined, and the integrated 3D model obtained can be used more efficiently.

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“…The resulting point cloud data contain highly accurate three-dimensional (3D) locations of topographic features of the roadway and nearby surrounding areas ( 15 ). Since manual processing of these data is labor intensive and time consuming, researchers are continuously working on developing automated methods to extract desirable objects or information such as trees ( 16 , 17 ), pole-shape objects ( 18 ), traffic signs ( 19 ), lane markings ( 12 , 20 ), and multiple objects ( 21 ) from these data.…”

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confidence: 99%

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

Shams

Sarasua

Russell

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Adequate water pavement surface drainage on highways is crucial in minimizing the potential of hydroplaning. Highway cross slope has a significant effect of draining water laterally from the pavement surface. Currently, field surveying techniques and other manual measurement methods are used to collect cross slope data on a limited basis in most states, despite these methods being labor intensive and exposing personnel to traffic. Furthermore, field surveying techniques cannot provide continuous data and can only be conducted at sample-based locations. This study conducted a technical evaluation of the effectiveness of airborne LiDAR (light detection and ranging) scanning and mobile terrestrial LiDAR scanning systems in measuring pavement cross slopes. Cross slope data were extracted from the LiDAR point cloud at five selected test sections using two different methods: (i) end-to-end method using elevations only from the pavement edge lines to generate the cross slope; and (ii) 0.2 ft interval point extraction along the cross-section and using a fitted linear regression line as the basis for the cross slope. Cross slopes were also measured at test section locations using conventional surveying methods and compared with LiDAR-extracted cross slopes. Results demonstrate that LiDAR methods are reliable for collecting accurate pavement cross slopes and should be considered for the purpose of cross slope verification on a braod scale such as statewide to address cross slope and pavement surface drainage issues proactively.

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A Robust and Efficient Method for Power Lines Extraction from Mobile LiDAR Point Clouds

Cited by 12 publications

References 50 publications

An enhanced descriptor extraction algorithm for power line detection from point clouds

An enhanced descriptor extraction algorithm for power line detection from point clouds

Accuracy Assessment Toward Merging of Terrestrial Laser Scanner Point Data and Unmanned Aerial System Point Data

Extracting Highway Cross Slopes From Airborne and Mobile LiDAR Point Clouds

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