Building Modeling; Classification; Digital Twins; Feature Extraction; Robust Regression; Semantic Segmentation; 3D point cloud; Building footprint; Building model; Features extraction; Laser scanning; Point cloud data; Robust regressions; Robust technique; Semantic segmentation; Volumetric 3D; Information Systems; Geography, Planning and Development
Abstract :
[en] The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD.
Disciplines :
Computer science
Author, co-author :
Nurunnabi, A. ; Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg
Teferle, N. ; Geodesy and Geospatial Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, Luxembourg
Balado, J.; CINTECX, GeoTECH Group, University of Vigo, Vigo, Spain
Chen, M.; Institute for Creative Technologies, University of Southern California, Los Angeles, United States
Poux, Florent ; Université de Liège - ULiège > Département de géographie > Unité de Géomatique - Topographie et géométrologie
Sun, C.; School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia
Language :
English
Title :
ROBUST TECHNIQUES FOR BUILDING FOOTPRINT EXTRACTION IN AERIAL LASER SCANNING 3D POINT CLOUDS
Publication date :
27 October 2022
Event name :
Urban Geoinformatics 2022
Event place :
Beijing, Chn
Event date :
01-11-2022 => 04-11-2022
Audience :
International
Journal title :
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISSN :
1682-1750
eISSN :
2194-9034
Publisher :
International Society for Photogrammetry and Remote Sensing
This study is with the Project 2019-05-030-24, SOLSTICE - Programme Fonds Européen de Developpment Régional (FEDER)/Ministère de l’Economie of the G. D. of Luxembourg.
AHN3: Actueel Hoogtebestand Nederland data: https://app.pdok.nl/ahn3-downloadpage/
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