Advancing urban air temperature mapping with machine learning

Urban heat is a significant contemporary challenge caused by the combined effect of urban development and global climate change. There has been substantial research investigating urban heat and assessing the effectiveness of heat mitigation strategies for different cities. Much of this research uses satellite-based Land Surface Temperature (LST) to assess urban heat through bird's-eye view surface temperatures, whereas canopy urban heat, measured by air temperature (Ta), is more directly relevant for public health and citizen thermal comfort. However, the sparse spatial coverage of Ta measurements fails to capture the significant temporal and spatial variability of air temperature in urban areas. Therefore, there is a need to produce gridded air temperature maps that represent these variations at scales relevant to people. Using crowdsourced air temperature measurements and machine learning techniques, we developed an innovative approach for estimating gridded air temperature for Sydney, Australia. We achieved this by using Landsat LST data as well as incorporating urban datasets characterizing urban form, fabric, and geography. A Convolutional Neural Network (CNN) was employed to infill gaps in the crowdsourced sensor data, achieving high performance and enhancing the spatial resolution of the temperature data. This study presents an effective approach for generating high-resolution, city-scale air temperature maps for cities aiming to enhance their temperature mapping and improve urban climate resilience.