GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction

• URL: http://arxiv.org/abs/2509.10308v1
• Date: Fri, 12 Sep 2025 14:50:56 GMT
• Title: GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction
• Authors: Joshua Dimasaka, Christian Geiß, Robert Muir-Wood, Emily So,
• Abstract summary: We introduce GraphCSVAE, a novel probabilistic data-driven framework for modeling physical vulnerability.<n>We introduce a weakly supervised first-order transition matrix that reflects changes in the distribution of physical vulnerability in two disaster-stricken and socioeconomically disadvantaged areas.<n>Our findings offer valuable insights into localizedtemporal auditing and sustainable strategies for post-disaster risk reduction.
• Score: 0.5949779668853555
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In the aftermath of disasters, many institutions worldwide face challenges in continually monitoring changes in disaster risk, limiting the ability of key decision-makers to assess progress towards the UN Sendai Framework for Disaster Risk Reduction 2015-2030. While numerous efforts have substantially advanced the large-scale modeling of hazard and exposure through Earth observation and data-driven methods, progress remains limited in modeling another equally important yet challenging element of the risk equation: physical vulnerability. To address this gap, we introduce Graph Categorical Structured Variational Autoencoder (GraphCSVAE), a novel probabilistic data-driven framework for modeling physical vulnerability by integrating deep learning, graph representation, and categorical probabilistic inference, using time-series satellite-derived datasets and prior expert belief systems. We introduce a weakly supervised first-order transition matrix that reflects the changes in the spatiotemporal distribution of physical vulnerability in two disaster-stricken and socioeconomically disadvantaged areas: (1) the cyclone-impacted coastal Khurushkul community in Bangladesh and (2) the mudslide-affected city of Freetown in Sierra Leone. Our work reveals post-disaster regional dynamics in physical vulnerability, offering valuable insights into localized spatiotemporal auditing and sustainable strategies for post-disaster risk reduction.

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