Fully convolutional neural networks (FCNs) are commonly used for semantic image segmentation, essentially the assignment of every pixel in an image to one of two or more categories. In this notebook we examine a popular FCN architecture, called UNet to perform a specific semantic segmentation task, namely urban building recognition: the identification within an arbitrarily complex remote sensing image of houses, schools, commercial edifices, etc.