scholarly journals AIRBORNE LIDAR POWER LINE CLASSIFICATION BASED ON SPATIAL TOPOLOGICAL STRUCTURE CHARACTERISTICS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 165-169
Author(s):  
Y. Wang ◽  
Q. Chen ◽  
K. Li ◽  
D. Zheng ◽  
J. Fang
Keyword(s):  
Data Processing ◽  
Power Line ◽  
Airborne Lidar ◽  
Power Lines ◽  
Automatic Extraction ◽  
Multi Scale ◽  
Topological Features ◽  
Detailed Evaluation ◽  
Airborne Lidar Data ◽  
Line Classification

Automatic extraction of power lines has become a topic of great importance in airborne LiDAR data processing for transmission line management. In this paper, we present a new, fully automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) neighbourhood selection, (iii) feature extraction based on spatial topology, and (iv) SVM classification. In a detailed evaluation involving seven neighbourhood definitions, 26 geometric features and two datasets, we demonstrated that the use of multi-scale neighbourhoods for individual 3D points significantly improved the power line classification. Additionally, we showed that the spatial topological features may even further improve the results while reducing data processing time.

scholarly journals Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Edisa Lozić ◽  
Benjamin Štular
Keyword(s):  
Data Processing ◽  
Good Practice ◽  
Airborne Lidar ◽  
Archaeological Prospection ◽  
Hard Data ◽  
Cloud Processing ◽  
Archaeological Interpretation ◽  
Point Cloud Processing ◽  
Data Acquisition And Processing ◽  
Airborne Lidar Data

Airborne LiDAR is a widely accepted tool for archaeological prospection. Over the last decade an archaeology-specific data processing workflow has been evolving, ranging from raw data acquisition and processing, point cloud processing and product derivation to archaeological interpretation, dissemination and archiving. Currently, though, there is no agreement on the specific steps or terminology. This workflow is an interpretative knowledge production process that must be documented as such to ensure the intellectual transparency and accountability required for evidence-based archaeological interpretation. However, this is rarely the case, and there are no accepted schemas, let alone standards, to do so. As a result, there is a risk that the data processing steps of the workflow will be accepted as a black box process and its results as “hard data”. The first step in documenting a scientific process is to define it. Therefore, this paper provides a critical review of existing archaeology-specific workflows for airborne LiDAR-derived topographic data processing, resulting in an 18-step workflow with consistent terminology. Its novelty and significance lies in the fact that the existing comprehensive studies are outdated and the newer ones focus on selected aspects of the workflow. Based on the updated workflow, a good practice example for its documentation is presented.

scholarly journals Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas

2017 ◽  
Vol 9 (8) ◽  
pp. 771 ◽  
Author(s):  
Yanjun Wang ◽  
Qi Chen ◽  
Lin Liu ◽  
Dunyong Zheng ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Urban Areas ◽  
Supervised Classification ◽  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

A multi-scale registration of urban aerial image with airborne lidar data

2015 ◽  
Author(s):  
Shuo Huang ◽  
Siying Chen ◽  
Yinchao Zhang ◽  
Pan Guo ◽  
He Chen
Keyword(s):  
Airborne Lidar ◽  
Aerial Image ◽  
Lidar Data ◽  
Multi Scale ◽  
Airborne Lidar Data

scholarly journals AUTOMATIC DETECTION AND CHARACTERISATION OF POWER LINES AND THEIR SURROUNDINGS USING LIDAR DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1161-1168 ◽  
Author(s):  
M. Yermo ◽  
J. Martínez ◽  
O. G. Lorenzo ◽  
D. L. Vilariño ◽  
J. C. Cabaleiro ◽  
...  
Keyword(s):  
Low Voltage ◽  
Parallel Implementation ◽  
Point Clouds ◽  
Power Line ◽  
Power Lines ◽  
Computational Time ◽  
Efficient Memory ◽  
3D Lidar ◽  
Very High ◽  
Line Classification

<p><strong>Abstract.</strong> Light Detection and Ranging (LiDAR) is nowadays one of the most used tools to obtain geospatial data. In this paper, a method to detect and characterise power lines of both high and low voltage and their surroundings from 3D LiDAR point clouds exclusively is proposed. First, to identify points of the power lines a global search of candidate points is carried out based on the height of each point compared to its neighbours. Then, the Hough Transform (HT) is applied on the set of candidate points to extract the catenaries that belong to each power line, allowing the identification of each conductor individually. Finally, conductors located on the same power line are grouped, their geometric characteristics analysed, and the quantitative features of the surroundings are computed. A very high accuracy of power line classification is reached with these methods, while the computational time is optimised by efficient memory usage and parallel implementation of the code.</p>

A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data

2021 ◽  
pp. 1-14
Author(s):  
Wen Li ◽  
Zhipeng Luo ◽  
Zhenlong Xiao ◽  
Yiping Chen ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Power Lines ◽  
Lidar Data ◽  
Airborne Lidar Data

scholarly journals Airborne LiDAR Point Cloud Processing for Archaeology. Pipeline and QGIS Toolbox

2021 ◽  
Vol 13 (16) ◽  
pp. 3225
Author(s):  
Benjamin Štular ◽  
Stefan Eichert ◽  
Edisa Lozić
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
State Of The Art ◽  
Airborne Lidar ◽  
Reproducible Research ◽  
Lidar Data ◽  
Archaeological Prospection ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Airborne Lidar Data

The use of topographic airborne LiDAR data has become an essential part of archaeological prospection. However, as a step towards theoretically aware, impactful, and reproducible research, a more rigorous and transparent method of data processing is required. To this end, we set out to create a processing pipeline for archaeology-specific point cloud processing and derivation of products that are optimized for general-purpose data. The proposed pipeline improves on ground and building point cloud classification. The main area of innovation in the proposed pipeline is raster grid interpolation. We have improved the state-of-the-art by introducing a hybrid interpolation technique that combines inverse distance weighting with a triangulated irregular network with linear interpolation. State-of-the-art solutions for enhanced visualizations are included and essential metadata and paradata are also generated. In addition, we have introduced a QGIS plug-in that implements the pipeline as a one-step process. It reduces the manual workload by 75 to 90 percent and requires no special skills other than a general familiarity with the QGIS environment. It is intended that the pipeline and tool will contribute to the white-boxing of archaeology-specific airborne LiDAR data processing. In discussion, the role of data processing in the knowledge production process is explored.

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