用图像预测飓风前后停电情况，助力弱势地区应急响应。
Multimodal Power Outage Prediction for Rapid Disaster Response and Resource Allocation
- 基于视觉时空图神经网络分析卫星图像，捕捉停电空间与时间模式。
- 可提前预判停电严重程度与具体位置，支持资源精准调配。
- 特别关注弱势社区，推动光伏等基础设施公平部署。
气候变化导致极端天气频发，威胁能源系统安全。尽管可再生能源转型至关重要，但受忽视的弱势社区往往在基础设施升级中滞后。本文提出一种新型视觉时空框架，用于预测飓风前后夜间灯光变化、停电严重程度与位置。核心方法为视觉-时空图神经网络（VST-GNN），从遥感图像中学习空间与时间上的连贯性。该研究旨在揭示亟需加强能源基建的地区，如未来光伏发电部署重点区域，提升政策制定者与社区利益相关方对脆弱电网的认知，增强高风险社区的能源韧性与可靠性。
原文摘要 · Abstract (English)
Extreme weather events are increasingly common due to climate change, posing significant risks. To mitigate further damage, a shift towards renewable energy is imperative. Unfortunately, underrepresented communities that are most affected often receive infrastructure improvements last. We propose a novel visual spatiotemporal framework for predicting nighttime lights (NTL), power outage severity and location before and after major hurricanes. Central to our solution is the Visual-Spatiotemporal Graph Neural Network (VST-GNN), to learn spatial and temporal coherence from images. Our work brings awareness to underrepresented areas in urgent need of enhanced energy infrastructure, such as future photovoltaic (PV) deployment. By identifying the severity and localization of power outages, our initiative aims to raise awareness and prompt action from policymakers and community stakeholders. Ultimately, this effort seeks to empower regions with vulnerable energy infrastructure, enhancing resilience and reliability for at-risk communities.
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