Keywords
Abstract
Digital urban governance depends on visual sensing systems that remain reliable under adverse weather and complex urban conditions. Rain removal and rainy-condition segmentation improve image quality and semantic interpretation for flood-prone cities, traffic monitoring, and emergency response. LiDAR-guided cross-attention fusion introduces a multimodal perspective by combining hyperspectral and LiDAR information for band selection and image classification. This is relevant to urban monitoring because flood adaptation increasingly depends on multisource spatial evidence. Pre-disaster relocation research provides the policy context, while polycentric development and urban sub-center studies explain where visual monitoring and adaptation interventions may be most valuable. LLM confidence research adds a decision-support dimension by highlighting how AI systems communicate uncertainty. Together, these references support urban governance systems that integrate weather-robust vision, multimodal sensing, spatial planning, and confidence-aware analytics.
References
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