Deep neural networks enhance urban land surface temperature data resolution

Researchers have developed deep neural networks to improve the resolution of land surface temperature (LST) data for urban areas. By combining data from geostationary and polar-orbiting satellites, they created LST fields with a 1 km resolution at 15-minute intervals. A U-Net model was trained to downscale SEVIRI/MSG data to MODIS resolution, achieving an RMSE of 1.92°C. Additionally, a ConvLSTM model was used for nowcasting LSTs up to 75 minutes ahead, outperforming benchmark models with RMSEs between 0.57°C and 1.15°C. AI

影响 Enhances urban climate modeling and satellite monitoring capabilities with higher-resolution temperature data.

排序理由 The cluster contains an academic paper detailing a new methodology and results in the field of machine learning applied to environmental science. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• SEVIRI/MSG
• Solomiia Kurchaba
• Terra/Aqua MODIS
• U-Net

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