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Deep neural networks enhance urban land surface temperature data resolution

By PulseAugur Editorial · [1 sources] ·

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

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

RANK_REASON 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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep neural networks enhance urban land surface temperature data resolution

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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]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Angela Meyer ·

    Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks

    Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies. Yet, existing satellite-derived LST products provide either high spatial or high temporal resolution, resulting in a fundamental trade-off between the two. To add…

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