Using Convolutional Neural Networks to detect features in sattelite images

In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets. It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods. If you have been following the latest technical developments you probably know that CNN's are used for face recognition, object detection, analysis of medical images, automatic inspection in manufacturing processes, natural language processing tasks, any many other applications. You could say that you're only limited by your imagination and creativity (and of course motivation, energy and time) to find practical applications for CNN's. Inspired by Kaggle's Sattelite Imagery Feature Detection challenge, I would like to find out how easy it is to detect features (roads in this particular case) in sattelite and aerial images.