  Large-scale vision foundation models have made significant progress in visual
tasks on natural images, with vision transformers being the primary choice due
to their good scalability and representation ability. However, large-scale
models in remote sensing (RS) have not yet been sufficiently explored. In this
paper, we resort to plain vision transformers with about 100 million parameters
and make the first attempt to propose large vision models tailored to RS tasks
and investigate how such large models perform. To handle the large sizes and
objects of arbitrary orientations in RS images, we propose a new rotated
varied-size window attention to replace the original full attention in
transformers, which can significantly reduce the computational cost and memory
footprint while learning better object representation by extracting rich
context from the generated diverse windows. Experiments on detection tasks show
the superiority of our model over all state-of-the-art models, achieving 81.24%
mAP on the DOTA-V1.0 dataset. The results of our models on downstream
classification and segmentation tasks also show competitive performance
compared to existing advanced methods. Further experiments show the advantages
of our models in terms of computational complexity and data efficiency in
transferring.
