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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