County-Level Hurricane Power Outage Risk Prediction Based on Spatial Support Transformation and a Dual-Relation Spatiotemporal Graph Network

County-level hurricane power outage risk prediction faces a mismatch in spatial support among hazard rasters, infrastructure exposure, and outage labels. Conventional county-mean aggregation can weaken the spatial association between local hazard peaks and transmission corridors. To address this issue, we propose the Raster-to-Grid Spatiotemporal GeoAI Network (R2G-STGeoNet). It first combines Sentinel-1 SAR, precipitation, wind fields, and geographic environmental information to encode multi-source hazard states. Raster-to-Grid Exposure Projection (R2GEP) then transforms continuous hazard fields into features including county exposure, along-line exposure, local peaks, and lengths of above-threshold exposure. A dual-relation spatiotemporal graph combining geographic adjacency and transmission-corridor relationships jointly predicts outage occurrence probability and outage rate. Using hurricanes as case studies, we evaluate matched-input baselines, structural ablations, cross-event transfer, and spatial holdout, accounting for missing labels and data latency. The full model achieves an AUROC of 0.913; replacing R2GEP with county means lowers AUROC to 0.867. With identical R2G inputs, the proposed model outperforms R2G-ST-GNN (AUROC = 0.895). The results indicate that spatial support transformation and infrastructure relation modeling improve county-level outage risk prediction under extreme weather.