  As computer vision before, remote sensing has been radically changed by the
introduction of Convolution Neural Networks. Land cover use, object detection
and scene understanding in aerial images rely more and more on deep learning to
achieve new state-of-the-art results. Recent architectures such as Fully
Convolutional Networks (Long et al., 2015) can even produce pixel level
annotations for semantic mapping. In this work, we show how to use such deep
networks to detect, segment and classify different varieties of wheeled
vehicles in aerial images from the ISPRS Potsdam dataset. This allows us to
tackle object detection and classification on a complex dataset made up of
visually similar classes, and to demonstrate the relevance of such a subclass
modeling approach. Especially, we want to show that deep learning is also
suitable for object-oriented analysis of Earth Observation data. First, we
train a FCN variant on the ISPRS Potsdam dataset and show how the learnt
semantic maps can be used to extract precise segmentation of vehicles, which
allow us studying the repartition of vehicles in the city. Second, we train a
CNN to perform vehicle classification on the VEDAI (Razakarivony and Jurie,
2016) dataset, and transfer its knowledge to classify candidate segmented
vehicles on the Potsdam dataset.
