Developing gridded air temperature data over cities using machine learning

Urban heat is a growing concern for public health, energy demand, and urban liveability. High-resolution air temperature (T _a ) data is needed for developing effective, localised adaptation strategies, but obtaining such data at the city scale remains a challenge as weather stations are often sparse and unevenly distributed within cities. To address this data gap, we developed a machine learning (ML) framework that creates high-resolution, gridded T _a maps using crowdsourced observations and diverse geospatial datasets describing the city. This framework uses satellite-derived Land Surface Temperature (LST), urban form and fabric datasets, and meteorological variables to train a Convolutional Neural Network (CNN) algorithm. The approach was implemented in Sydney, Australia, using multi-day observations from 2019 to 2024, producing 30 m gridded T _a estimates with high accuracy (R² = 0.97, RMSE = 0.91°C, surpassing previously reported ML performances). We further assessed the generalisation and spatial transferability of this method, which revealed that the CNN model maintained strong predictive accuracy for unseen locations across the city (R² = 0.91–0.93; RMSE = 1.27–1.44°C). Model performance also remained stable when the number of T _a stations was significantly reduced by ~ 80%. Performance slightly declined for unseen days (R² = 0.66–0.93; RMSE = 1.52–2.60°C), suggesting the need for incorporating a broader range of weather conditions. We find that high-resolution city-descriptive datasets are beneficial but not essential, as comparable accuracy was achieved using only globally available predictors. These results indicate that the proposed framework is transferable to other cities, including those in data-sparse regions. The study provides an effective and scalable approach for developing city-scale air temperature maps, which are urgently needed for urban heat assessment, climate adaptation, and public health planning.