A machine learning approach can automate maps’ production with relevant features in a short timeframe and from disparate data sources. Our training data contains individual tiles of satellite imagery in RGB format, and labels (color segmentation super-imposed on the images) are used to annotate recognized information. The goal is to train a model, which is given a new tile (satellite image), to annotate all buildings. The typical training dataset has 280,741 tiles (as 300×300-pixel RGB images), so we restrict our test case to 0.5% in the interest of speed.