We focus on the novel problem of estimating the spatial resolution of overhead imagery. More and more overhead imagery is becoming available without such meta-data either because it was not collected in the first place or was not preserved with the imagery. We propose a bottom-up, data-driven approach using convolutional neural networks. We show that an extended model which incorporates dilated convolution to expand the receptive field of the network outperforms a baseline model on an evaluation dataset with a range of simulated spatial resolutions. We make a number of interesting observations to motivate future work on this novel problem.