  Vehicle detection in aerial and satellite images is still challenging due to
their tiny appearance in pixels compared to the overall size of remote sensing
imagery. Classical methods of object detection very often fail in this scenario
due to violation of implicit assumptions made such as rich texture, small to
moderate ratios between image size and object size. Satellite video is a very
new modality which introduces temporal consistency as inductive bias.
Approaches for vehicle detection in satellite video use either background
subtraction, frame differencing or subspace methods showing moderate
performance (0.26 - 0.82 $F_1$ score). This work proposes to apply recent work
on deep learning for wide-area motion imagery (WAMI) on satellite video. We
show in a first approach comparable results (0.84 $F_1$) on Planet's SkySat-1
LasVegas video with room for further improvement.
