A CNN based functional zone classification method for aerial images

Urban functional zones refer to areas (or regions) of a city which provide specific urban functions for peoples who lived in the city. The spatial layout of buildings in functional zone show a specific pattern, e.g. residual areas usually have similar builds and the positions of which are highly organized. In this paper, we show that it is possible to identify urban functional zones from a remote sensed imagery. To this end, a convolutional neural networks (CNN) based functional zone classification method is proposed. The method mainly consists of three steps. Firstly, the aerial imagery of the city is partitioned into disjoint regions by road network. Then, each region is further divided into patches and is fed to a fully connected CNN. The output of which is considered as distributions of this patches on the five previously defined functional zones. Finally, we take a vote strategy to identify the function zone of this region. We test our method on a collection of Google Earth images over Shenyang, Beijing, etc. The results demonstrate the effectiveness of the proposed method.