Floods are among the most frequent and damaging disasters worldwide, affecting millions of people and causing high economic losses. Traditional ground-based monitoring systems often lack sufficient coverage, particularly in vulnerable regions, creating a need for more reliable tools. Satellite-based Earth Observation (EO) has emerged as a vital resource for flood detection, monitoring, and management. Satellites including Sentinel-1, Sentinel-2, Landsat, and commercial constellations like Planetscope, Skysat, COSMO-SkyMed provide valuable datasets to map inundation, assess damage, and guide emergency response. This thesis focuses on the Emilia-Romagna floods of May 2023, one of Italy’s most severe in recent decades, particularly in Spazzate-Sassatelli. Using Sentinel-2 (open-source) and SkySat (commercial) imagery, a modular Python-based algorithm was developed to process images based on K-means clustering machine learning method.