Learning to Map Nearly Anything

@article{Salem2019LearningTM,
  title={Learning to Map Nearly Anything},
  author={Tawfiq Salem and Connor Greenwell and Hunter Blanton and Nathan B. Jacobs},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.06928}
}
Looking at the world from above, it is possible to estimate many properties of a given location, including the type of land cover and the expected land use. Historically, such tasks have relied on relatively coarse-grained categories due to the difficulty of obtaining fine-grained annotations. In this work, we propose an easily extensible approach that makes it possible to estimate fine-grained properties from overhead imagery. In particular, we propose a cross-modal distillation strategy to… CONTINUE READING

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