DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience
This work addresses urban climate resilience for city planners and environmental scientists, but it is incremental as it applies a novel method to a specific domain.
The paper tackled the problem of understanding how urban land use affects local climate by developing DeepLCZChange, a deep learning model that correlates LiDAR data with Landsat 8 surface temperature, and demonstrated a cooling effect of urban forests in New York with specific numerical results.
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.