Summary of Seamo: a Multi-seasonal and Multimodal Remote Sensing Foundation Model, by Xuyang Li and Danfeng Hong and Chenyu Li and Jocelyn Chanussot
SeaMo: A Multi-Seasonal and Multimodal Remote Sensing Foundation Model
by Xuyang Li, Danfeng Hong, Chenyu Li, Jocelyn Chanussot
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Visual Foundation Model (VFM) called SeaMo is proposed for Earth observation in remote sensing (RS) data. SeaMo integrates multi-seasonal and multimodal information from RS data, leveraging masked image modeling with non-aligned cropping techniques, multi-source inputs, and temporal-multimodal fusion blocks. This comprehensive model can extract spatial properties, assimilate multi-seasonal data, and perform exceptionally well on various geoscience tasks. The superiority of SeaMo is validated through extensive ablation studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SeaMo is a new type of computer program that helps us understand the Earth better using special kinds of pictures taken from space. These pictures have lots of information in them, but they are very big and hard to work with. SeaMo is designed to take all this information and use it to help us do things like predict weather patterns or find signs of natural disasters. It’s a really powerful tool that can be used for many different tasks. |
