  This work presents a deep learning approach for vehicle detection in
satellite video. Vehicle detection is perhaps impossible in single EO satellite
images due to the tininess of vehicles (4-10 pixel) and their similarity to the
background. Instead, we consider satellite video which overcomes the lack of
spatial information by temporal consistency of vehicle movement. A new
spatiotemporal model of a compact $3 \times 3$ convolutional, neural network is
proposed which neglects pooling layers and uses leaky ReLUs. Then we use a
reformulation of the output heatmap including Non-Maximum-Suppression (NMS) for
the final segmentation. Empirical results on two new annotated satellite videos
reconfirm the applicability of this approach for vehicle detection. They more
importantly indicate that pre-training on WAMI data and then fine-tuning on few
annotated video frames for a new video is sufficient. In our experiment only
five annotated images yield a $F_1$ score of 0.81 on a new video showing more
complex traffic patterns than the Las Vegas video. Our best result on Las Vegas
is a $F_1$ score of 0.87 which makes the proposed approach a leading method for
this benchmark.
