城市气候监测和热风险分析团队终于有了一个兼顾传感器预算和布局约束的实用方案——GNN在稀疏数据下比传统插值法更准，做城市热岛效应或极端高温预警的可以直接参考。
该研究提出一种不确定性感知的图神经网络框架，用于从稀疏传感器重建城市每日最高温度场，并支持距离约束的传感器布局和概率超限映射。模型采用图注意力机制和均值-残差架构，通过高斯负对数似然训练，同时预测温度场和空间变化的不确定性场。在蒙特利尔地区的实验中，使用Daymet v4.1数据（1公里分辨率）和严格的时间留出验证，该GNN在10-40个传感器预算下均优于反距离加权和普通克里金法。传感器布局影响在低预算时显著，约30个传感器时达到饱和。该框架为不确定性感知的温度场重建和面向决策的热风险制图提供了有效工具。
Uncertainty-Aware Graph Neural Reconstruction of Urban Temperature Fields from Sparse Sensors under Deployment Constraints
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 predictive uncertainty field using a graph-attention-based mean-residual architecture trained with a Gaussian negative log-likelihood. Sensor placement is addressed using a Proper Orthogonal Decomposition with QR factorization (POD-QR) strategy with a 4 km minimum inter-sensor distance constraint and is compared with random feasible placement and farthest-point sampling. The framework is evaluated over a Montreal-area polygon using Daymet v4.1 daily temperature data (1 km resolution) under a strict temporal hold-out protocol (training: 2020-2023; testing: 2024). Across sensor budgets (10-40 sensors), the proposed GNN consistently outperforms inverse distance weighting and ordinary kriging in RMSE and MAE on unobserved nodes. Sensor-placement effects are most pronounced at low budgets and diminish at higher budgets, with a practical saturation regime emerging around 30 sensors under the imposed spacing constraint. Probabilistic evaluation further shows improved uncertainty calibration with increasing sensor density and a better sharpness-calibration trade-off than kriging. These results support the proposed framework as an effective tool for uncertainty-aware temperature field reconstruction and decision-oriented heat-risk mapping.