Classification of high-voltage power line structures in low density ALS data acquired over broad non-urban areas

Airborne laser scanning (ALS) has gained importance over recent decades for multiple uses related to the cartography of landscapes. Processing ALS data over large areas for forest resource estimation and ecological assessments requires efficient algorithms to filter out some points from the raw data and remove human-made structures that would otherwise be mistaken for natural objects. In this paper, we describe an algorithm developed for the segmentation and cleaning of electrical network facilities in low density (2.5 to 13 points/m2) ALS point clouds. The algorithm was designed to identify transmission towers, conductor wires and earth wires from high-voltage power lines in natural landscapes. The method is based on two priors i.e. (1) the availability of a map of the high-voltage power lines across the area of interest and (2) knowledge of the type of transmission towers that hold the conductors along a given power line. It was tested on a network totalling 200 km of wires supported by 415 transmission towers with diverse topographies and topologies with an accuracy of 98.6%. This work will help further the automated detection capacity of power line structures, which had previously been limited to high density point clouds in small, urbanised areas. The method is open-source and available online.

[1]  S. Goetz,et al.  Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA , 2006 .

[2]  Gunho Sohn,et al.  3D CLASSIFICATION OF POWER-LINE SCENE FROM AIRBORNE LASER SCANNING DATA USING RANDOM FORESTS , 2010 .

[3]  Ross Nelson,et al.  How did we get here? An early history of forestry lidar1 , 2013 .

[4]  Yoonseok Jwa,et al.  AUTOMATIC POWERLINE SCENE CLASSIFICATION AND RECONSTRUCTION USING AIRBORNE LIDAR DATA , 2012 .

[5]  Bela Stantic,et al.  An Automated Method for Individual Wire Extraction from Power Line Corridor using LiDAR Data , 2019, 2019 Digital Image Computing: Techniques and Applications (DICTA).

[6]  Nicholas C. Coops,et al.  lidR: An R package for analysis of Airborne Laser Scanning (ALS) data , 2020 .

[7]  Yoonseok Jwa,et al.  AUTOMATIC 3D POWERLINE RECONSTRUCTION USING AIRBORNE LiDAR DATA , 2009 .

[8]  David Auty,et al.  Airborne LiDAR Data Manipulation and Visualization for Forestry Applications [R package lidR version 3.0.4] , 2020 .

[9]  M. Nilsson.  Estimation of tree heights and stand volume using an airborne lidar system , 1996 .

[10]  Alen Hatibović.  Derivation of Equations for Conductor and Sag Curves of an Overhead Line Based on a Given Catenary Constant , 2014 .

[11]  Extraction of Power Lines and Pylons from LiDAR Point Clouds Using a GCN-Based Method , 2020, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.

[12]  Shuang Song,et al.  Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data , 2018, Remote. Sens..

[13]  Jing Liang,et al.  A New Power-Line Extraction Method Based on Airborne LiDAR Point Cloud Data , 2011, 2011 International Symposium on Image and Data Fusion.

[14]  Kai Li,et al.  Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas , 2017, Remote. Sens..

[15]  Richard A. Fournier,et al.  Predicting wood fiber attributes using local-scale metrics from terrestrial LiDAR data: A case study of Newfoundland conifer species , 2015 .

[16]  Richard A. Fournier,et al.  Predicting wood quantity and quality attributes of balsam fir and black spruce using airborne laser scanner data , 2014 .

[17]  Gongping Wu,et al.  A Novel Method to Reconstruct Overhead High-Voltage Power Lines Using Cable Inspection Robot LiDAR Data , 2017, Remote. Sens..

[18]  E. Næsset.  Determination of mean tree height of forest stands using airborne laser scanner data , 1997 .

[19]  Nicholas C. Coops,et al.  Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data: Increasing Spatial Detail and Optimizing Yield Curve Selection through Template Matching , 2016 .

[20]  C. Briese,et al.  Extraction and Modeling of Power Lines from ALS Point Clouds , 2004 .

[21]  Xiangguo Lin,et al.  Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests , 2017, Remote. Sens..

[22]  Gui-Song Xia,et al.  Detecting Power Lines in UAV Images with Convolutional Features and Structured Constraints , 2019, Remote. Sens..

[23]  H. B. Kim.  RANDOM FORESTS BASED MULTIPLE CLASSIFIER SYSTEM FOR POWERLINE SCENE CLASSIFICATION , 2011 .

[24]  R. Fournier,et al.  Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data , 2015 .

[25]  Juntao Yang,et al.  Voxel-Based Extraction of Transmission Lines From Airborne LiDAR Point Cloud Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Rodney A. Walker,et al.  Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors , 2009, 2009 Digital Image Computing: Techniques and Applications.

[27]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[28]  Xiaojing Yao,et al.  Airborne LiDAR point cloud classification with global-local graph attention convolution neural network , 2021 .

[29]  G. Sohn,et al.  A Piecewise Catenary Curve Model Growing for 3D Power Line Reconstruction , 2012 .

[30]  Maggi Kelly,et al.  A New Method for Segmenting Individual Trees from the Lidar Point Cloud , 2012 .

[31]  G. Sohn,et al.  AUTOMATIC 3 D POWERLINE RECONSTRUCTION USING AIRBORNE LiDAR DATA , 2009 .

[32]  Juha Hyyppä,et al.  Remote sensing methods for power line corridor surveys , 2016 .

[33]  Joanne C. White,et al.  A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach , 2013 .

[34]  Fevzi Karsli,et al.  Automatic boundary extraction of inland water bodies using LiDAR data , 2015 .

[35]  G. Sohn,et al.  Point-based Classification of Power Line Corridor Scene Using Random Forests , 2013 .

[36]  Robert A. McLaughlin,et al.  Extracting transmission lines from airborne LIDAR data , 2006, IEEE Geoscience and Remote Sensing Letters.

[37]  Tao Guo,et al.  Research on Point Cloud Power Line Segmentation and fitting algorithm , 2019, 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC).

[38]  G. Sohn,et al.  Random Forests Based Multiple Classifier System for Power-Line Scene Classification , 2012 .

[39]  C. Mallet,et al.  Large-scale road detection in forested mountainous areas using airborne topographic lidar data , 2016 .

[40]  Juha Hyyppä,et al.  Fully-Automated Power Line Extraction from Airborne Laser Scanning Point Clouds in Forest Areas , 2014, Remote. Sens..

[41]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .