{"ID":2871829,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10308","arxiv_id":"2509.10308","title":"GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction","abstract":"In the aftermath of disasters, many institutions worldwide face challenges in monitoring changes in disaster risk, limiting assessment of 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 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 expert priors. We introduce a weakly supervised first-order transition matrix to capture changes in the spatiotemporal distribution of vulnerability across two disaster-affected and socioeconomically disadvantaged regions: the cyclone-impacted Khurushkul community in Bangladesh and the mudslide-affected city of Freetown in Sierra Leone. Across both case studies, the framework constructs large-scale graph representations spanning 2016-2023 and evaluates posterior compositional distributions against expert priors using Aitchison distance due to the lack of temporal groundtruth labels. The work reveals post-disaster regional dynamics in physical vulnerability, offering valuable insights into localized spatiotemporal auditing and sustainable strategies for post-disaster risk reduction.","short_abstract":"In the aftermath of disasters, many institutions worldwide face challenges in monitoring changes in disaster risk, limiting assessment of 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 thr...","url_abs":"https://arxiv.org/abs/2509.10308","url_pdf":"https://arxiv.org/pdf/2509.10308v2","authors":"[\"Joshua Dimasaka\",\"Christian Geiß\",\"Robert Muir-Wood\",\"Emily So\"]","published":"2025-09-12T14:50:56Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}