What Goes Where: Predicting Object Distributions from Above

  title={What Goes Where: Predicting Object Distributions from Above},
  author={Connor Greenwell and Scott Workman and Nathan Jacobs},
  journal={IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium},
In this work, we propose a cross-view learning approach, in which images captured from a ground-level view are used as weakly supervised annotations for interpreting overhead imagery. The outcome is a convolutional neural network for overhead imagery that is capable of predicting the type and count of objects that are likely to be seen from a ground-level perspective. We demonstrate our approach on a large dataset of geotagged ground-level and overhead imagery and find that our network captures… CONTINUE READING


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