DeepUrbanDownscale: A physics informed deep learning framework for high-resolution urban surface temperature estimation via 3D point clouds

Linwei Chen, Bowen Fang, Lei Zhao, Yu Zang, Weiquan Liu, Yiping Chen, Cheng Wang, Jonathan Li

Research output: Contribution to journalArticlepeer-review


Accurate high-resolution downscaling of surface climate variables (such as surface temperature) over urban areas has long been a critical yet unresolved research problem in the field of urban climate and environmental sciences. In this paper, we propose a novel physics informed neural network (PINN) based framework: DeepUrbanDownscale (DUD) for high-resolution urban surface temperature estimation. Anchored in process-based modeling and satellite remote sensing, the DUD network leverages the high-precision 3D point clouds to achieve accurate urban land surface temperature (LST) estimation at an ultra-high spatial resolution. This network, ingesting the high-precision land surface geometry information derived from 3D point clouds and guided by the atmospheric physics related to surface temperature, constructs a physics informed data-driven framework to fit high-resolution temperature distribution, which is otherwise difficult to be obtained by physical (numerical) simulations or traditional machine learning. Specifically, the proposed DUD network contains two branches: The Global Feature Perception (GPFP) branch and Local Urban Surface Perception (LUSP) branch. The former considers the broader-scale urban physical parameters, constraining the estimation results in accordance with the relevant physical laws. The latter, by employing a proposed local spatial coefficient index (LSCI), which is based on 3D point clouds, the estimation performance is further improved at a very high resolution. Results from designed experiments demonstrate that the proposed DUD network predicts the urban LST on a 30-by-30 m grid with the estimated error less than 0.2 Kelvin compared to the satellite measurement, which is well below the errors of other traditional methods.

Original languageEnglish (US)
Article number102650
JournalInternational Journal of Applied Earth Observation and Geoinformation
StatePublished - Feb 2022


  • 3D point cloud
  • Deep urban downscale
  • Physics informed neural network

ASJC Scopus subject areas

  • Global and Planetary Change
  • Earth-Surface Processes
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law


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