Summary of M3leo: a Multi-modal, Multi-label Earth Observation Dataset Integrating Interferometric Sar and Multispectral Data, by Matthew J Allen et al.
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data
by Matthew J Allen, Francisco Dorr, Joseph Alejandro Gallego Mejia, Laura Martínez-Ferrer, Anna Jungbluth, Freddie Kalaitzis, Raúl Ramos-Pollán
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 multi-modal, multi-label Earth observation dataset called M3LEO is introduced, which includes polarimetric, interferometric, and coherence SAR data derived from Sentinel-1, alongside multispectral Sentinel-2 imagery and auxiliary terrain property information. The dataset comprises approximately 17M 4×4 km data chips spanning six diverse geographic regions. A PyTorch Lightning framework is also provided, configured using Hydra to accommodate its use across various ML applications in Earth observation. This setup allows seamless integration with popular platforms like Google Earth Engine. The paper demonstrates a substantial distribution shift in self-supervised embeddings across geographic regions, even when controlling for terrain properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big library of images taken from space! These images can help us understand our planet better and solve problems like climate change. But there are so many pictures that it’s hard to make sense of them all. Scientists created a special tool called M3LEO that helps organize these images in a way that makes it easier for computers to use them. This tool includes lots of different types of images taken from space, as well as information about the terrain below. It’s like having a superpower to understand our planet! |
Keywords
» Artificial intelligence » Multi modal » Self supervised
