Building Modeling; City Modeling; Classification; Digital Twins; Feature Extraction; Robust Regression; Semantic Segmentation
Résumé :
[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.
Centre de recherche :
Department of Geodesy and Geospatial Engineering, FSTM, UL
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
NURUNNABI, Abdul Awal Md ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
TEFERLE, Felix Norman ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Balado, Jesus; CINTECX, GeoTECH Group > University of Vigo, Spain
Chen, Meida; 3Institute for Creative Technologies, > University of Southern California, Los Angeles, USA
Poux, Florent; Geomatics Unit, Faculty of Science > University of Liège, Belgium
Sun, Chayn; School of Mathematical and Geospatial Sciences > RMIT University, Melbourne, Australia
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
Date de publication/diffusion :
novembre 2022
Nom de la manifestation :
Gi4DM 2022 & Urban Geoinformatics 2022
Lieu de la manifestation :
Beijing, Chine
Date de la manifestation :
from 01-11-2022 to 04-11-2022
Manifestation à portée :
International
Titre de l'ouvrage principal :
Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
AHN3: Actueel Hoogtebestand Nederland data: https://app.pdok.nl/ahn3-downloadpage/
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