The Use of Random Forest for the Classification of Point Cloud in Urban Scene
The Use of Random Forest for the Classification of Point Cloud in Urban Scene |
||
 |
 |
|
| © 2024 by IJETT Journal | ||
| Volume-72 Issue-3 |
||
| Year of Publication : 2024 | ||
| Author : Vincenzo Saverio Alfio, Massimiliano Pepe, Domenica Costantino |
||
| DOI : 10.14445/22315381/IJETT-V72I3P101 | ||
How to Cite?
Vincenzo Saverio Alfio, Massimiliano Pepe, Domenica Costantino, "The Use of Random Forest for the Classification of Point Cloud in Urban Scene ," International Journal of Engineering Trends and Technology, vol. 72, no. 3, pp. 1-9, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I3P101
Abstract
The aim of the paper concerns the classification of the point cloud using a suitable method based on the Random Forest algorithm. In addition, thanks to the use of specific sensors, such as Airborne Laser Scanning (ALS), it is possible to obtain a georeferenced point cloud in a short time, which makes it possible to represent and model urban areas not only through a graphic representation but also through a semantic one. The development of a suitable methodology made it possible to automatically classify a point cloud acquired on an urban scene acquired with a “Leica City Mapper” sensor over the city of Bordeaux (France). The quality of the point cloud classification was evaluated using appropriate performance indices (Overall Accuracy and F1 measure), which showed encouraging results on the quality of the developed method.
Keywords
ALS, Random Forest, Point Cloud, Classification, F1 score, Overall Accuracy
References
[1] Valeria-Ersilia Oniga et al., “3D Modeling of Urban Area Based on Oblique UAS Images-An End-to-End Pipeline,” Remote Sensing, vol. 14, no. 2, pp. 1-31, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Surendra Pal Singh, Kamal Jain, and V. Ravibabu Mandla, “3D Scene Reconstruction from Video Camera for Virtual 3D City Modeling,” American Journal of Engineering Research, vol. 3, no. 1, pp. 140-148, 2014.
[Google Scholar] [Publisher Link]
[3] Lipeng Gao et al., “Novel Framework for 3D Road Extraction Based on Airborne LiDAR and High-Resolution Remote Sensing Imagery,” Remote Sensing, vol. 13, no. 23, pp. 1-22, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Massimiliano Pepe, Vincenzo Saverio Alfio, and Domenica Costantino, “UAV Platforms and the SfM-MVS Approach in the 3D Surveys and Modelling: A Review in the Cultural Heritage Field,” Applied Sciences, vol. 12, no. 24, pp. 1-35, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Massimiliano Pepe, Luigi Fregonese, and Nicola Crocetto, “Use of SfM-MVS Approach to Nadir and Oblique Images Generated throught Aerial Cameras to Build 2.5 D Map and 3D Models in Urban Areas,” Geocarto International, vol. 37, no. 1, pp. 120-141, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Kuangen Zhang et al., “Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features,” arXiv, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Eleonora Grilli, “Automatic Texture- and Geometry-based Classification Methods Applied to Cultural Heritage,” Bulletin of the Italian Society of Photogrammetry and Topography, no. 1, pp. 8-16, 2019.
[Google Scholar] [Publisher Link]
[8] Eleonora Grilli, and Fabio Remondino, “Machine Learning Generalisation across Different 3D Architectural Heritage,” ISPRS International Journal of Geo-Information, vol. 9, no. 6, pp. 1-19, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Leo Breiman, “Random Forests,” Machine Learning, vol. 45, pp. 5-32, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Doudou Xue et al., “An Improved Random Forest Model Applied to Point Cloud Classification,” IOP Conference Series: Materials Science and Engineering, vol. 768, no. 7, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Huan Ni, Xiangguo Lin, and Jixian Zhang, “Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests,” Remote Sensing, vol. 9, no. 3, pp. 1-34, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yijie Lu et al., “Integration of Optical, SAR and DEM Data for Automated Detection of Debris-Covered Glaciers over the Western Nyainqentanglha Using a Random Forest Classifier,” Cold Regions Science and Technology, vol. 193, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Harith Aljumaily et al., “Point Cloud Voxel Classification of Aerial Urban LiDAR using Voxel Attributes and Random Forest Approach,” International Journal of Applied Earth Observation and Geoinformation, vol. 118, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Meida Chen et al., “3D Photogrammetry Point Cloud Segmentation using a Model Ensembling Framework,” Journal of Computing in Civil Engineering, vol. 34, no. 6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mustafa Zeybek, “Classification of UAV Point Clouds by Random Forest Machine Learning Algorithm,” Turkish Journal of Engineering, vol. 5, no. 2, pp. 48-57, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] E. Grilli, F. Menna, and F. Remondino, “A Review of Point Clouds Segmentation and Classification Algorithms,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 339-344, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jun Wang, and Jie Shan, “Segmentation of LiDAR Point Clouds for Building Extraction,” American Society for Photogrammetry 9 Remote Sensing Annual Conference, Baltimore, MD, pp. 1-13, 2009.
[Google Scholar]
[18] Lina Yang et al., “Efficient Plane Extraction using Normal Estimation and RANSAC from 3D Point Cloud,” Computer Standards & Interfaces, vol. 82, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Wei Song et al., “CNN-Based 3D Object Classification using Hough Space of LiDAR Point Clouds,” Human-Centric Computing and Information Sciences, vol. 10, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] P. Tutzauer, D. Laupheimer, and N. Haala, “Semantic Urban Mesh Enhancement Utilizing a Hybrid Model,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 4, pp. 175-182, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] E. Özdemir, F. Remondino, and A. Golkar, “Aerial Point Cloud Classification with Deep Learning and Machine Learning Algorithms,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, pp. 843- 849, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Bisheng Yang et al., “Hierarchical Extraction of Urban Objects from Mobile Laser Scanning Data,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 99, pp. 45-57, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Yuan Li, Bo Wu, and Xuming Ge, “Structural Segmentation and Classification of Mobile Laser Scanning Point Clouds with Large Variations in Point Density,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 153, pp. 151-165, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Domenica Costantino et al., “Strategies for 3D Modelling of Buildings from Airborne Laser Scanner and Photogrammetric Data Based on Free-Form and Model-Driven Methods: The Case Study of the Old Town Centre of Bordeaux (France),” Applied Sciences, vol. 11, no. 22, pp. 1-24, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] M. Mohamed, S. Morsy, and A. El-Shazly, “Machine Learning for Mobile LIDAR Data Classification of 3D Road Environment,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 44, pp. 113-117, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Massimiliano Pepe, and Cluadio Parente, “Burned Area Recognition by Change Detection Analysis using Images Derived from Sentinel-2 Satellite: The Case Study of Sorrento Peninsula, Italy,” Journal of Applied Engineering Science, vol. 16, no. 2, pp. 225-232, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Shamsudeen Temitope Yekeen, Abdul‐Lateef Balogun, and Khamaruzaman B. Wan Yusof, “A Novel Deep Learning Instance Segmentation Model for Automated Marine Oil Spill Detection,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, pp. 190-200, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Massimiliano Pepe et al., “A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery,” ISPRS International Journal of Geo-Information, vol. 10, no. 10, pp. 1-17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Daniel Girardeau-Montaut, “CloudCompare,” France: EDF R&D Telecom ParisTech, 2016.
[Google Scholar]
[30] Michael Kölle et al., “The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo,” ISPRS Open Journal of Photogrammetry and Remote Sensing, vol. 1, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[31] N. Haala et al., “Hybrid Georeferencing, Enhancement and Classification of Ultra-Highresolution UAV LiDAR and Image Point Clouds for Monitoring Applications,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 2, pp. 727-734, 2020.
[CrossRef] [Google Scholar] [Publisher Link]