The Earth observation satellites have allowed us to monitor the planet at a scale that seemed unattainable up to some years ago. Satellite imagery plays a fundamental role in many applications, like forestry, tracking wildfires, mapping deforestation, railway industry, urban planning or agriculture. Terabytes of image data are produced everyday, making it necessary to have automated methods to recognise anomalies inside the images before using them for further analysis. One of the main obstacles in satellite imagery is represented by the presence of clouds, which regularly cover 66% of the Earth's surface, obscuring the ground objects we are interested in. This thesis studies the use of Deep Neural Networks to design an on-board algorithm able to perform cloud segmentation in real time with the aim of classifying each pixel of the satellite images as cloud or background.