In this article we present a combination of marked point processes with convolutional neural networks applied to remote sensing. While point processes allow modeling interactions between objects via priors, classical methods rely on contrast measures that become unreliable as objects of interest and context become more diverse. We propose learning likelihood measures using convolutional neural networks to make these measures more versatile and resilient. We apply our method to the detection of vehicles in satellite images.