Remote Sensing Vision-Language Foundation Models without Annotations via
  Ground Remote Alignment
Utkarsh Mall Cheng Perng Phoo Meilin Kelsey Liu Carl Vondrick Bharath Hariharan Kavita Bala
We introduce a method to train vision-language models for remote-sensing
images without using any textual annotations. Our key insight is to use
co-located internet imagery taken on the ground as an intermediary for
connecting remote-sensing images and language. Specifically, we train an image
encoder for remote sensing images to align with the image encoder of CLIP using
a large amount of paired internet and satellite images. Our unsupervised
approach enables the training of a first-of-its-kind large-scale vision
language model (VLM) for remote sensing images at two different resolutions. We
show that these VLMs enable zero-shot, open-vocabulary image classification,
retrieval, segmentation and visual question answering for satellite images. On
each of these tasks, our VLM trained without textual annotations outperforms
existing VLMs trained with supervision, with gains of up to 20% for
classification and 80% for segmentation.