Climatic Micro-zonation of Naples (Italy) Through Landsat and Machine Learning

Naples, in Southern Italy, is densely populated and severely impacted by several environmental risks associated with meteorological conditions. Because of the city’s varied topography, meteorological hazards vary significantly across its territory. Heatwaves have become more common over the last decade; the creation of thermal maps would aid in understanding the different hazards within the city and provide the community with more precise information on the associated health concerns. In this study, we use Landsat data to find locations with comparable temperature anomalies throughout the year. We then use a machine-learning technique to detect seasonal similarities. We discovered that our approach aids in the identification of low-quality data and the formation of geographical clusters with similar temperature anomalies.