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A Big Data Approach for 3D Building Extraction from Aerial Laser Scanning
Date Issued
2016-05
Date Available
2016-02-05T12:59:22Z
Abstract
This paper proposes a Big Data approach to automatically identify and extract buildings from a digital surface model created from aerial laser scanning data. The approach consists of two steps. The first step is a MapReduce process where neighboring points in a digital surface model are mapped into cubes. The second step uses a non-MapReduce algorithm first to remove trees and other obstructions and then to extract adjacent cubes. According to this approach, all adjacent cubes belong to the same object and an object is a set of adjacent cubes that belong to one or more adjacent buildings. Finally, an evaluation study is presented for a section of Dublin, Ireland to demonstrate the applicability of the approach resulting in a 92% quality level for the extraction of 106 buildings over 1 km2 including buildings that had more than 10 adjacent components of different heights and complicated roof geometries. The proposed approach is notable not only for its Big Data context but its usage of vector data.
Sponsorship
European Research Council
Science Foundation Ireland
Type of Material
Journal Article
Publisher
American Society of Civil Engineers
Journal
Journal of Computing in Civil Engineering
Volume
30
Issue
3
Copyright (Published Version)
2016 American Society of Civil Engineers
Web versions
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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29
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