DeepLCZChange: A REMOTE SENSING DEEP LEARNING MODEL ARCHITECTURE FOR URBAN CLIMATE RESILIENCECRediT

被引：0
|
作者：
Sun, Wenlu [1 ]
Sun, Yao [1 ]
Liu, Chenying [1 ,2 ]
Albrecht, Conrad M. [2 ]
机构：
[1] Tech Univ Munich, Data Sci Earth Observat, Munich, Germany
[2] German Aerosp Ctr, Remote Sensing Technol Inst, Cologne, Germany
来源：
IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年
关键词：
urban planning; local climate zones; climate resilience; LiDAR; Landsat; 8; deep neural network architecture; explainable artificial intelligence;
D O I：
10.1109/IGARSS52108.2023.10281573
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
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.
引用
收藏
页码：3616 / 3619
页数：4
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