Reference : Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/53425

Title :	Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
Language :	English
Author, co-author :	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]
Publication date :	Nov-2022
Main document title :	Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
Peer reviewed :	Yes
Audience :	International
Event name :	Gi4DM 2022 & Urban Geoinformatics 2022
Event date :	from 01-11-2022 to 04-11-2022
Event place (city) :	Beijing
Event country :	Chaina
Keywords :	[en] Building Modeling ; City Modeling ; Classification ; Digital Twins ; Feature Extraction ; Robust Regression ; Semantic Segmentation
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.
Research centres :	Department of Geodesy and Geospatial Engineering, FSTM, UL
Name of the research project :	SOLISTICE
Target :	Researchers ; Professionals ; Students ; Others
Permalink :	http://hdl.handle.net/10993/53425
Other URL :	https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-3-W2-2022/43/2022/

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	Fulltext file(s): File Commentary Version Size Access Open access Building footprint extraction_aN etal_isprs-archives-XLVIII-3-W2-2022-43-2022.pdf Publisher postprint 2.12 MB View/Open

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