DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for
  Urban Climate Resilience
Wenlu Sun Yao Sun Chenying Liu Conrad M Albrecht
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.