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 learn to predict the distribution of fine-grained properties from overhead imagery, without requiring any manual annotation of overhead imagery. We show that our learned models can be used directly for applications in mapping and image localization.
@article{arxiv.1909.06928,
title = {Learning to Map Nearly Anything},
author = {Tawfiq Salem and Connor Greenwell and Hunter Blanton and Nathan Jacobs},
journal= {arXiv preprint arXiv:1909.06928},
year = {2019}
}