Connor Greenwell

What Goes Where: Predicting Object Distributions From Above

Connor Greenwell, Scott Workman, Nathan Jacobs. (IGARSS, 2018)

Abstract

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 semantically meaningful features, despite being trained without manual annotations.

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Bibtex

@inproceedings{greenwell2018objects,
  author = {Greenwell, Connor and Workman, Scott and Jacobs, Nathan},
  title = {What Goes Where: Predicting Object Distributions From Above},
  year = {2018},
  booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  annotation = {CAREER}
}