Remote Sensing and Machine Learning for Urban Air Quality and Heat Island Monitoring

Abstract. This study presents a dual-strategy approach to monitor urban environmental stressors, conducted within the ASI-MUR-funded Space It Up! project, focusing on atmospheric pollution and the urban heat island (UHI) effect. First, we developed a scalable machine learning (ML) framework for estimating ground-level concentrations of NO₂, SO₂, and CO in Milan using Sentinel-5P satellite data, ERA5 reanalysis, CAMS forecasts, and ARPA Lombardia ground measurements. Data preprocessing pipelines were optimized by switching to Google Earth Engine, reducing retrieval times and enabling operational scalability. Despite known satellite retrieval limitations in winter months for SO₂, model performance remained robust, with normalized RMSE values consistently below 0.85. For CO, a Deep Attention Network achieved the best results (NRMSE = 0.4879), demonstrating the adaptability of the framework across pollutants. Additionally, a comparative analysis of low-cost air quality sensors showed high performance from AirGradient devices, particularly for PM_2.5 and temperature, though significant inter-brand discrepancies were observed for CO₂. Second, we implemented an advanced LCZ classification method integrating hyperspectral PRISMA imagery, Sentinel-2 data, and urban canopy parameters (UCPs). Applied to the Metropolitan City of Milan, the proposed workflow achieved substantial improvements over existing methods, with an overall accuracy increase up to 16% when utilizing PRISMA data compared to the state-of-art LCZ Generator approach. We also presented ongoing efforts to further improve the proposed methodology, including the automation of data retrieval and training and test sample creation. The methodology is being applied across multiple urban areas worldwide by also testing other ML techniques. Together, these methodologies provide a comprehensive and reproducible framework for urban environmental monitoring.