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arXiv Open Access 2023

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

Wenlu Sun Yao Sun Chenying Liu Conrad M Albrecht
Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.CV cs.AI cs.LG

Penulis (4)

W

Wenlu Sun

Y

Yao Sun

C

Chenying Liu

C

Conrad M Albrecht

Format Sitasi

APA MLA BibTeX
Sun, W., Sun, Y., Liu, C., Albrecht, C.M. (2023). DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience. https://arxiv.org/abs/2306.06269

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓