DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience

• URL: http://arxiv.org/abs/2306.06269v1
• Date: Fri, 9 Jun 2023 21:42:29 GMT
• Title: DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience
• Authors: Wenlu Sun, Yao Sun, Chenying Liu, Conrad M Albrecht
• Abstract summary: We present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.
• Score: 2.8037951156321372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban land use structures impact local climate conditions of metropolitan areas. To shed light on the mechanism of local climate wrt. urban land use, we present a novel, data-driven deep learning architecture and pipeline, DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8 satellite's surface temperature product. A proof-of-concept numerical experiment utilizes corresponding remote sensing data for the city of New York to verify the cooling effect of urban forests.

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