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arXiv · 2306.06269

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

Wenlu Sun·Yao Sun·Chenying Liu·Conrad M Albrecht
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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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Wenlu Sun, Yao Sun, Chenying Liu, Conrad M Albrecht. 2023-06-09. DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience. https://arxiv.org/abs/2306.06269

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