Reference : Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/10993/53425
Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
English
Nurunnabi, Abdul Awal Md mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Teferle, Felix Norman mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Balado, Jesus mailto [CINTECX, GeoTECH Group > University of Vigo, Spain]
Chen, Meida mailto [3Institute for Creative Technologies, > University of Southern California, Los Angeles, USA]
Poux, Florent mailto [Geomatics Unit, Faculty of Science > University of Liège, Belgium]
Sun, Chayn mailto [School of Mathematical and Geospatial Sciences > RMIT University, Melbourne, Australia]
Nov-2022
Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds
Yes
International
Gi4DM 2022 & Urban Geoinformatics 2022
from 01-11-2022 to 04-11-2022
Beijing
Chaina
[en] Building Modeling ; City Modeling ; Classification ; Digital Twins ; Feature Extraction ; Robust Regression ; Semantic Segmentation
[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.
Department of Geodesy and Geospatial Engineering, FSTM, UL
SOLISTICE
Researchers ; Professionals ; Students ; Others
http://hdl.handle.net/10993/53425
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVIII-3-W2-2022/43/2022/

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