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

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Joshua Dimas · 2026-05-21 · via cs.LG updates on arXiv.org

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。原文来自 — 版权归原作者所有。

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

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