Satellite images are often a good base for computer vision. Usually big areas are mapped and photos are frequently updated, therefore the data is most likely up to date. Image recognition in this field is very advanced and a lot of data is freely available so the network can be trained well. Because of this, it is possible to recognize for example: roads, railways and rivers. Other objects can be detected as well, such as: cars, greenery and the status of meadows or even nature reserves. Like in many projects the challenge with deep learning is to provide correctly annotated data to train the deep learning network. Therefore it needs to know what is correct and incorrect so that the system can learn from that. Because objects often already contain GPS coordinates it is relatively easy to learn as long as the sources are usable.