GeoOutageKG: A Multimodal Geospatiotemporal Knowledge Graph for Multiresolution Power Outage Analysis
Ethan Frakes, Yinghui Wu, Roger H. French, Mengjie Li

TL;DR
GeoOutageKG is a multimodal knowledge graph integrating diverse geospatiotemporal outage data sources, enabling detailed analysis and prediction of power outages at multiple resolutions, especially useful for disaster response and risk assessment.
Contribution
The paper introduces GeoOutageKG, a novel, modular, and reusable semantic resource that combines satellite imagery, high-resolution outage maps, and county-level reports for comprehensive outage analysis.
Findings
Over 10.6 million outage records included
Integration of multimodal data improves outage detection
Enables multiresolution geospatiotemporal analysis
Abstract
Detecting, analyzing, and predicting power outages is crucial for grid risk assessment and disaster mitigation. Numerous outages occur each year, exacerbated by extreme weather events such as hurricanes. Existing outage data are typically reported at the county level, limiting their spatial resolution and making it difficult to capture localized patterns. However, it offers excellent temporal granularity. In contrast, nighttime light satellite image data provides significantly higher spatial resolution and enables a more comprehensive spatial depiction of outages, enhancing the accuracy of assessing the geographic extent and severity of power loss after disaster events. However, these satellite data are only available on a daily basis. Integrating spatiotemporal visual and time-series data sources into a unified knowledge representation can substantially improve power outage detection,…
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