Analysis and Verification of Building Changes Based on Point Clouds from Different Sources and Time Periods

Detecting changes in buildings over time is an important issue in monitoring urban areas, landscape changes, assessing natural disaster risks or updating geospatial databases. Three-dimensional (3D) information derived from dense image matching or laser data can effectively extract changes in buildi...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-03, Vol.15 (5), p.1414
Main Authors: Marmol, Urszula, Borowiec, Natalia
Format: Article
Language:English
Subjects:
Algorithms
Automation
building
Buildings
Change detection
Classification
Deep learning
Disaster risk
image filtering
Image quality
Land use
Lidar
LiDAR system
Matching
Morphological filters
Natural disasters
Neural networks
Optical radar
Photogrammetry
Poland
Remote sensing
SfM
Urban areas
Urban development
Urbanization
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Summary:Detecting changes in buildings over time is an important issue in monitoring urban areas, landscape changes, assessing natural disaster risks or updating geospatial databases. Three-dimensional (3D) information derived from dense image matching or laser data can effectively extract changes in buildings. This research proposes an automated method for detecting building changes in urban areas using archival aerial images and LiDAR data. The archival images, dating from 1970 to 1993, were subjected to a dense matching procedure to obtain point clouds. The LiDAR data came from 2006 and 2012. The proposed algorithm is based on height difference-generated nDSM. In addition, morphological filters and criteria considering area size and shape parameters were included. The study was divided into two sections: one concerned the detection of buildings from LiDAR data, an issue that is now widely known and used; the other concerned an attempt at automatic detection from archived aerial images. The automation of detection from archival data proved to be complex, so issues related to the generation of a dense point cloud from this type of data were discussed in detail. The study revealed problems of archival images related to the poor identification of ground control points (GCP), insufficient overlap between images or poor radiometric quality of the scanned material. The research showed that over the 50 years, the built-up area increased as many as three times in the analysed area. The developed method of detecting buildings calculated at a level of more than 90% in the case of the LiDAR data and 88% based on the archival data.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15051414