Characterization and modeling of power line corridor elements from LiDAR point clouds

Ortega, Sebastián; Trujillo, Agustín; Santana, J.J.; Suárez, José P.; Santana, Jaisiel

doi:10.1016/j.isprsjprs.2019.03.021

Cited by 39 publications

(33 citation statements)

References 18 publications

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“…Early researches into the subject were directed towards the extraction [14][15][16][17][18] and 3D reconstruction [19][20][21][22][23] of power lines from LiDAR data. Milzer and Briese (2004), for example, proposed minimum linkage clustering for pylons' extraction, while the extraction of power lines between them was achieved by using 2D Hough Transform, followed by 3D line fitting.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, Guo et al proposed Joint Boost-based classification of pylons [22] and power lines [21], and their reconstruction was achieved by using random sample consensus (RANSAC). Finally, Ortega et al [23] performed a reconstruction of wires based on the catenary equation using particle swarm optimisation after an initial classification of pylons and wires, and their segmentation into individual conductors. Accordingly, while these traditional methods achieved mapping of pylons, followed by recognition of wires, more recent approaches focused on improving their performances [24] and extraction of more detailed information, such as, for example, the reconstruction of bundle conductors [25].…”

Section: Introductionmentioning

confidence: 99%

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A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

et al. 2021

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This paper presents the first complete approach to achieving environmental intelligence support in the management of vegetation within electrical power transmission corridors. Contrary to the related studies that focused on the mapping of power lines, together with encroaching vegetation risk assessment, we realised predictive analytics with vegetation growth simulation. This was achieved by following the JDL/DFIG data fusion model for complementary feature extraction from Light Detection and Ranging (LiDAR) derived data products and auxiliary thematic maps that feed an ensemble regression model. The results indicate that improved vegetation growth prediction accuracy is obtained by segmenting training samples according to their contextual similarities that relate to their ecological niches. Furthermore, efficient situation assessment was then performed using a rasterised parametrically defined funnel-shaped volumetric filter. In this way, RMSE≈ 1 m was measured when considering tree growth simulation, while a 0.37 m error was estimated in encroaching vegetation detection, demonstrating significant improvements over the field observations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

et al. 2021

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“…Among the many methods for obtaining 3D point clouds, airborne laser scanning (ALS) or light detection and ranging (LiDAR) are important technologies for obtaining high-precision and dense point clouds of large-scale ground scenes. Many applications of ALS point clouds have been explored, such as digital elevation model (DEM) generation [1,2], building reconstruction [3,4], road extraction [5,6], forest mapping [7,8], power line monitoring [9,10] and so on. For these applications, the basic and critical step is the classification of the 3D point cloud, which is also called semantic segmentation of the point cloud in the field of computer vision.…”

Section: Introductionmentioning

confidence: 99%

Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network

Zhu

Sui

Zang

et al. 2021

IJGI

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In various applications of airborne laser scanning (ALS), the classification of the point cloud is a basic and key step. It requires assigning category labels to each point, such as ground, building or vegetation. Convolutional neural networks have achieved great success in image classification and semantic segmentation, but they cannot be directly applied to point cloud classification because of the disordered and unstructured characteristics of point clouds. In this paper, we design a novel convolution operator to extract local features directly from unstructured points. Based on this convolution operator, we define the convolution layer, construct a convolution neural network to learn multi-level features from the point cloud, and obtain the category label of each point in an end-to-end manner. The proposed method is evaluated on two ALS datasets: the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen 3D Labeling benchmark and the 2019 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest (DFC) 3D dataset. The results show that our method achieves state-of-the-art performance for ALS point cloud classification, especially for the larger dataset DFC: we get an overall accuracy of 97.74% and a mean intersection over union (mIoU) of 0.9202, ranking in first place on the contest website.

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“…NURBS fitting use NURBS curve and surfaces to fit a point cloud, which is useful in curved lines and surfaces including power lines [26,27], complex building exterior and curved pipes [28,29], etc.…”

Section: Introductionmentioning

confidence: 99%

Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction

Hu,

Zhao,

Huang

et al. 2019

Preprint

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A learning-based 3D reconstruction method for long-span bridges is proposed in this paper. 3D reconstruction generates a 3D computer model of a real object or scene from images, it involves many stages and open problems. Existing point-based methods focus on generating 3D point clouds and their reconstructed polygonal mesh or fitting-based geometrical models in urban scenes civil structures reconstruction within Manhattan world constrains and have made great achievements. Difficulties arise when an attempt is made to transfer these systems to structures with complex topology and part relations like steel trusses and long-span bridges, this could be attributed to point clouds are often unevenly distributed with noise and suffer from occlusions and incompletion, recovering a satisfactory 3D model from these highly unstructured point clouds in a bottom-up pattern while preserving the geometrical and topological properties makes enormous challenge to existing algorithms. Considering the prior human knowledge that these structures are in conformity to regular spatial layouts in terms of components, a learning-based topology-aware 3D reconstruction method which can obtain highlevel structural graph layouts and low-level 3D shapes from images is proposed in this paper. We demonstrate the feasibility of this method by testing on two real long-span steel truss cable-stayed bridges.

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Characterization and modeling of power line corridor elements from LiDAR point clouds

Cited by 39 publications

References 18 publications

A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

A Complete Environmental Intelligence System for LiDAR-Based Vegetation Management in Power-Line Corridors

Classification of Airborne Laser Scanning Point Cloud Using Point-Based Convolutional Neural Network

Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction

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