Integrating Remote Sensing for Earthquake Risk Assessment in Istanbul's Kartal District

Developing strategies to mitigate damage from high-risk buildings in earthquake-prone areas is essential. This study focuses on prioritizing buildings based on their risk levels, considering the impracticality of addressing all high-risk structures simultaneously. Traditional methods emphasize individual buildings, overlooking the collective impact. This study extends beyond earthquake effects, considering financial losses due to displacements particularly in energy-dependent contexts. Advancements in remote sensing, particularly SAR technology's phase and amplitude signal characteristics, are crucial. These technologies facilitate shell deformation analysis, classification studies, and post-disaster damage mapping, offering broad spatial coverage and all-weather, day-and-night operation. Our study employs Sentinel 1 SBAS method, Tandem-X Change detection maps, building construction dates, and VS30 data to analyze Istanbul's Kartal District. Utilizing Microsoft's deep learning-based building footprints, seismic VS30 data, TanDEM-X Change Maps, and building age and height information, this research comprehensively assesses earthquake risks. The SBAS technique with Sentinel 1 data is applied for surface displacement analysis, enhancing the understanding of earthquake losses and updating ground deformation data for Istanbul.