Voxel Change: Big Data–Based Change Detection for Aerial Urban LiDAR of Unequal Densities
Publication: Journal of Surveying Engineering
Volume 147, Issue 4
Abstract
The proposed voxel change (VC) algorithm provides accurate, scalable, and quantifiable change detection for urban aerial Light Detection and Ranging (LiDAR) scans. This VC algorithm uses MapReduce, a big data programming model, to map neighboring points into cubes. The algorithm converts each data set into a group of cubes, and classifies them into categories of building, ground, or vegetation. It then compares and quantifies changes in area or volume. Spatial discontinuity is overcome by clustering. Quality metrics are demonstrated by comparing a data set of Dublin, Ireland, using a 2007 scan with a point density of 225 points per square meter () and a 2015 scan with (totaling more than 500 million points). By using only positional LiDAR information as the data input, the quality metric exceeded 90% across the full data set with respect to lost, new, and unchanged designations for vegetation, buildings, and ground areas, and regularly exceeded 98% for buildings. The technique successfully processes nonrectilinear features and robustly provides a quantification of change for both building expansion and vegetation at a level using dense, modern data sets.
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Data Availability Statement
Some or all data, models, and code generated or used during the study are available in a repository or online in accordance with funder data retention policies: https://doi.org/10.7925/drs1.ucdlib_30467 (Laefer et al. 2014); https://doi.org/10.17609/N8MQ0N (Laefer et al. 2017).
Acknowledgments
The Dublin data were acquired with funding from the European Research Council (ERC-2012-StG-307836) and additional funding from Science Foundation Ireland (12/ERC/I2534 and 05/PICA/I830).
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Published In
Journal of Surveying Engineering
Volume 147 • Issue 4 • November 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 15, 2020
Accepted: Feb 23, 2021
Published online: Sep 9, 2021
Published in print: Nov 1, 2021
Discussion open until: Feb 9, 2022
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