A Big Data Approach for 3D Building Extraction from Aerial Laser Scanning

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Title: A Big Data Approach for 3D Building Extraction from Aerial Laser Scanning
Authors: Aljumaily, Harith
Laefer, Debra F.
Cuadra, Dolores
Permanent link: http://hdl.handle.net/10197/7450
Date: May-2016
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.
Funding Details: 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
Keywords: Building extractionMapReduceBig dataLiDARDigital surface modelAerial laser scanning
DOI: 10.1061/(ASCE)CP.1943-5487.0000524
Other versions: http://cedb.asce.org
Language: en
Status of Item: Peer reviewed
metadata.dc.date.available: 2016-02-05T12:59:22Z
Appears in Collections:Earth Institute Research Collection
Civil Engineering Research Collection

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