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Estimating The Spatial Resolution of Overhead Imagery Using Convolutional Neural Networks

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.