GraphCSVAE: Graph Categorical Structured Variational Autoencoder for Spatiotemporal Auditing of Physical Vulnerability Towards Sustainable Post-Disaster Risk Reduction
Domaine:
climategeospatialType de record:
paperdatasetsoftware
Créateur:
Mui
MuiHôte:
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. Accepted for publication in Progress in Disaster Science (on May 20, 2026) and at the 8th International Disaster and Risk Conference, IDRC 2025 | Keywords: weakly supervised, graph, categorical, vulnerability, remote sensing, spatiotemporal | The data and code are respectively available at A Local-Scale Dataset of An… and github.com
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