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.
| Comments: | 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 this https URL and this https URL |
| Subjects: | Machine Learning (cs.LG) |
| MSC classes: | 68T07, 68R10, 60E05, 60J10, 86A32, 86A15 |
| Cite as: | arXiv:2509.10308 [cs.LG] |
| (or arXiv:2509.10308v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2509.10308 arXiv-issued DOI via DataCite |
From: Joshua Dimasaka [view email]
[v1]
Fri, 12 Sep 2025 14:50:56 UTC (7,828 KB)
[v2]
Wed, 20 May 2026 08:22:05 UTC (7,764 KB)
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