Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images

We propose a novel weakly supervised approach for creating maps using free-form textual descriptions (or captions). We refer to this new line of work of creating textual maps as zero-shot mapping. Prior works have approached mapping tasks by developing models that predict over a fixed set of attributes using overhead imagery. However, these models are very restrictive as they can only solve highly specific tasks for which they were trained. Mapping text, on the other hand, allows us to solve a large variety of mapping problems with minimal restrictions. To achieve this, we train a contrastive learning framework called Sat2Cap on a new large-scale dataset of paired overhead and ground-level images. For a given location, our model predicts the expected CLIP embedding of the ground-level scenery. Sat2Cap is also conditioned on temporal information, enabling it to learn dynamic concepts that vary over time. Our experimental results demonstrate that our models successfully capture fine-grained concepts and effectively adapt to temporal variations. Our approach does not require any text-labeled data making the training easily scalable. The code, dataset, and models will be made publicly available.