Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints
arXiv:2606.02038v1 Announce Type: cross Abstract: Reconstructing spatially continuous daily temperature fields from sparse observations is important for urban climate monitoring and heat-risk analysis, but practical deployments are limited by sensor budgets and spacing constraints. This study proposes an uncertainty-aware graph neural network (GNN) framework for reconstructing daily maximum temperature fields from sparse sensors while supporting distance-constrained sensor placement and probabilistic exceedance mapping. The model predicts both the temperature field and a spatially varying pred
The increasing availability of urban sensor networks and advancements in graph neural networks make this type of applied AI research feasible and highly relevant to immediate urban challenges.
Accurate, fine-grained urban temperature monitoring is critical for climate adaptation, infrastructure planning, and real-time public health interventions, especially as heatwaves intensify globally.
This development moves beyond simple data collection to predictive, uncertainty-aware, and actionable intelligence for urban climate resilience, enabling more strategic resource allocation.
- · Smart city initiatives
- · Urban planners
- · Climate scientists
- · Public health organizations
- · Cities with inadequate sensor infrastructure
- · Traditional, less precise climate modeling approaches
Improved real-time heat-risk assessments and heat-island mitigation strategies in urban environments.
More targeted energy consumption reduction during heatwaves due to dynamic cooling policies informed by precise temperature maps.
Enhanced AI-driven urban design and material science that actively optimizes for thermal regulation and human comfort.
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Read at arXiv cs.LG