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Open AccessArticle

Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430076, China
2
Geography and GIS Department, Faculty of Arts, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3307; https://doi.org/10.3390/rs12203307
Received: 27 August 2020 / Revised: 1 October 2020 / Accepted: 3 October 2020 / Published: 12 October 2020
(This article belongs to the Special Issue Ground Deformation Patterns Detection by InSAR and GNSS Techniques)
Buildings are vulnerable to collapse incidents. We adopt a workflow to detect unusual vertical surface motions before building collapses based on PS-InSAR time series analysis and spatiotemporal data mining techniques. Sentinel-1 ascending and descending data are integrated to decompose vertical deformation in the city of Alexandria, Egypt. Collapsed building data were collected from official sources, and overlayed on PS-InSAR vertical deformation results. Time series deformation residuals are used to create a space–time cube in the ArcGIS software environment and analyzed by emerging hot spot analysis to extract spatiotemporal patterns for vertical deformation around collapsed buildings. Our results show two spatiotemporal patterns of new cold spot or new hot spot before the incidents in 66 out of 68 collapsed buildings between May 2015 and December 2018. The method was validated in detail on four collapsed buildings between January and May 2019, proving the applicability of this workflow to create a temporal vulnerability map for building collapse monitoring. This study is a step forward to create a PS-InSAR based model for building collapse prediction in the city. View Full-Text
Keywords: building collapse; land subsidence; permanent scatterers interferometry (PSI); Sentinel-1; spatiotemporal data mining; surface deformation building collapse; land subsidence; permanent scatterers interferometry (PSI); Sentinel-1; spatiotemporal data mining; surface deformation
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MDPI and ACS Style

Mohamadi, B.; Balz, T.; Younes, A. Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt. Remote Sens. 2020, 12, 3307.

AMA Style

Mohamadi B, Balz T, Younes A. Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt. Remote Sensing. 2020; 12(20):3307.

Chicago/Turabian Style

Mohamadi, Bahaa; Balz, Timo; Younes, Ali. 2020. "Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt" Remote Sens. 12, no. 20: 3307.

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