GeoAI machine learning-based intelligent downscaling seasonal characterization and analysis method for surface temperature

Through a GeoAI machine learning method, combined with satellite images and urban landscape characteristics, surface temperature data is reconstructed and seasonal analysis is carried out, which solves the problems of spatial and temporal continuity and high spatial resolution of surface temperature data in urban thermal environment research, and accurately characterizes and analyzes urban thermal environments.

CN119832442AActive Publication Date: 2025-04-15ZHENGZHOU UNIV
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Patent Information

Application Number
CN202411880541.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-19
Publication Date
2025-04-15
Estimated Expiration
2044-12-19

AI Technical Summary

Technical Problem

The existing technology is difficult to achieve spatial and temporal continuity and high spatial resolution of surface temperature data in urban thermal environment research, and the urban spatial characteristics are not accurate enough, which affects the accurate assessment of urban thermal environment.

Method used

Using a GeoAI machine learning method, combined with satellite images and urban landscape characteristics, surface temperature data are reconstructed and seasonal analysis is performed. The method includes generating smart city building and road indices using Landsat-8 and Sentianl-2 satellite data through multi-space scale U-Net models and random forest models, and performing reconstruction of daily surface temperature products and calculation of seasonal variation indices.

Benefits of technology

It realizes the spatiotemporal continuity and high spatial resolution of surface temperature data, provides accurate urban thermal environment characterization and analysis capabilities, and supports scientific urban planning and governance strategies.

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Abstract

The invention develops a surface temperature intelligent downscaling seasonal characterization and analysis method based on GeoAI machine learning. According to the method, interpolation and downscaling can be carried out on a surface temperature missing area influenced by cloud layer factors through urban landscape characteristic index products and incomplete satellite surface temperature inversion data. Through a specific stratified sampling mechanism, the problems of training deviation and over-fitting caused by insufficient representativeness of a data training set are solved, and a surface temperature product which takes seasons as units and has a 10m resolution ratio and a complete space-time sequence is generated. Verification shows that the method can accurately compensate for surface temperature data loss caused by factors such as cloud cover, ensures space integrity and time continuity of the surface temperature data, truly reflects the surface temperature of a research area when a satellite passes, and effectively represents seasonal space-time evolution of an urban thermal environment. Besides, the invention further develops a GeoAI-based feature importance intelligent evaluation framework, landscape features strongly related to the surface temperature can be analyzed and screened, scientific support and reference are provided for accurate characterization, modeling and optimization of the urban thermal environment, and the method has important application value.
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