High-Precision Intracity Temperature Estimation Based on Generated Point Clouds

GuanJie Huang, KaiQun He, Xiaoyue Lyu, Yu Zang, Hang Shu, Lei Zhao, Jonathan Li, Cheng Wang

Abstract: Estimation of urban surface temperature is crucial for urban planning and emergency management. Due to the complexity of intracity structures, it is very challenging to acquire satisfied prediction errors of the land surface temperature (LST) at very high resolution, like 60-by-60 m. Considering this, we propose a low-cost method for generating urban point clouds via readily accessible city data. Then we design an efficient descriptor, geofeature distribution matrix (GFDM) to describe the complex intracity structure. Using GFDM, we introduce a 3-D urban structure guided temperature prediction network (3D-UP Net) to capture the complex relationship between urban structure, upper atmospheric conditions, and surface temperature. The proposed 3D-UP Net is generalizable, capable of predicting future surface temperature for existing cities and even for those that are planned. Experiments conducted in multiple regions of China demonstrate that our method’s error is less than 1.5 K (in most cases) at a high resolution (60-by-60 m).