Spatio-Temporal SwinMAE: A Swin Transformer based Multiscale Representation Learner for Temporal Satellite Imagery
<|reference_start|>Spatio-Temporal SwinMAE: A Swin Transformer based Multiscale Representation Learner for Temporal Satellite Imagery: Currently, the foundation models represented by large language models have made dramatic progress and are used in a very wide range of domains including 2D and 3D vision. As one of the important application domains of foundation models, earth observation has attracted attention and various approaches have been developed. When considering earth observation as a single image capture, earth observation imagery can be processed as an image with three or more channels, and when it comes with multiple image captures of different timestamps at one location, the temporal observation can be considered as a set of continuous image resembling video frames or medical SCAN slices. This paper presents Spatio-Temporal SwinMAE (ST-SwinMAE), an architecture which particularly focuses on representation learning for spatio-temporal image processing. Specifically, it uses a hierarchical Masked Auto-encoder (MAE) with Video Swin Transformer blocks. With the architecture, we present a pretrained model named Degas 100M as a geospatial foundation model. Also, we propose an approach for transfer learning with Degas 100M, which both pretrained encoder and decoder of MAE are utilized with skip connections added between them to achieve multi-scale information communication, forms an architecture named Spatio-Temporal SwinUNet (ST-SwinUNet). Our approach shows significant improvements of performance over existing state-of-the-art of foundation models. Specifically, for transfer learning of the land cover downstream task on the PhilEO Bench dataset, it shows 10.4\% higher accuracy compared with other geospatial foundation models on average.<|reference_end|>
@article{nakayama2024satswinmae:,
    title={SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite
  Imagery},
    author={Yohei Nakayama, Jiawei Su, Luis M. Pazos-Out'on},
    journal={arXiv preprint arXiv:2405.02512},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.02512},
    primaryClass={cs.CV cs.AI}
}