###### Abstract  
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. building collapse; land subsidence; permanent scatterers interferometry (PSI); Sentinel-1; spatiotemporal data mining; surface deformation +
Footnote †: journal: _remote sensing_  
0000-0001-5000-5000-5000][PERSON] 0000-0001-5002-0002][PERSON] 0000-0002-0002-0003-0002][PERSON] 0000-0002-0003-0002-0003-0003][PERSON] 0000-0002-0002-0003-0003-0003-0003-0003-0003-0003-0003-0003-  
in addition, locally the technique can reach down to building scale [6]. Results of PS-InSAR were validated for building deformation monitoring and found to have similar millimeter-level accuracy as traditional leveling technique [7]. In this study, we use PS-InSAR time series results within known collapsed buildings in the city of Alexandria, Egypt, to detect spatiotemporal patterns of vertical deformation right before collapse incidents. Our hypothesis is that the land surface surrounding a collapsed building shows a spatiotemporal pattern of deformation before the collapse. Such patterns would allow for a collapse prediction and therefore a warning system. The patterns detected in this study are tested for the possibility of using as one of the inputs for a building collapse prediction model for collapsed buildings in known vertical deformation regions. Early on, [PERSON] et al. [8] used PS-InSAR to study surface motion within a building before its collapse incident. This was followed by studies interested in building deformation monitoring using InSAR techniques in many countries, including Italy [2; 6], France [8; 9], Poland and Russia [10], China [7; 11; 12], and South Korea [1]. However, very few have been applied to already collapsed buildings as the earliest paper, to test the connection of collapse incidents to surface instability [10]. Most of those studies focused on a limited number of buildings and very small areas of interest, except for [1; 2; 6]. The high resolution and relative short revisit time of X-band, for example, four days for COSMO-SkyMed (CSK), makes it very useful to use in building motion monitoring [1; 7; 9; 12]. C-band has also taken its share in this application [2; 6; 8; 10; 11], especially, with the availability of Sentinel-1 data offering global coverage. The literature reveals the higher applicability of SAR X-band data in building damage monitoring studies because of its higher resolution and shorter wavelength, which allows detection of more PS points per building [9]. In this study, the data availability was the reason to use C-band SAR data, as Sentinel-1 open-data coverage over the city of Alexandria is relatively good. Previous studies depended only on InSAR measurements to evaluate building stability without any additional spatial analysis or modeling approach. Only [PERSON] et al.