Learning to Map Nearly Anything
This work addresses the challenge of fine-grained property estimation in remote sensing, offering an extensible solution for mapping applications.
The paper tackles the problem of estimating fine-grained properties from overhead imagery without manual annotations by proposing a cross-modal distillation strategy, achieving models applicable to mapping and image localization.
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.