Detecting Roads from Aerial Images using Deep Learning
In recent years, Deep Neural Networks have been used to generate state-of-the-art results in numerous sub-fields of computer vision. This category of algorithms can be used to extract substantial amount of information from many types of imagery. Our work is focuses on training a neural network to accurately detect roads, by analyzing tens of thousands of satellite images. In the mapping industry, having very detailed and accurate maps is of crucial importance in order to be able to build high-quality and precise routing applications. The first step towards achieving this goal is detecting all the roads in a certain area. Every other piece of traffic-relevant map information, such as turn restrictions or speed limits, depends on knowing the underlying road network.
