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 localized high hazard values and transmission corridors. To address this problem, this study proposes the Raster-to-Grid Spatiotemporal GeoAI Network (R2G-STGeoNet). The method first integrates Sentinel-1 SAR, precipitation, wind fields, and geographical information to encode multisource hazard states. Raster-to-Grid Exposure Projection (R2GEP) then transforms continuous hazard fields into features describing county exposure, along-line exposure, local high values, and above-threshold exposure length. 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 conduct common-input baseline comparisons, structural ablations, cross-event transfer, and spatial holdout validation while considering missing labels and data latency. The full model achieves an AUROC of 0.913 in retrospective reconstruction, while the availability-constrained one-hour-ahead evaluation yields an AUROC of 0.884, decreasing to 0.862 when dynamic inputs unavailable at issuance are masked. Replacing R2GEP with county means reduces AUROC to 0.867, and the proposed model outperforms R2G-ST-GNN (AUROC = 0.895) under identical R2G inputs. The results indicate that spatial support transformation and infrastructure-relation modeling can improve county-level outage risk prediction under extreme weather.