Summary of Geoformer: a Vision and Sequence Transformer-based Approach For Greenhouse Gas Monitoring, by Madhav Khirwar and Ankur Narang
GeoFormer: A Vision and Sequence Transformer-based Approach for Greenhouse Gas Monitoring
by Madhav Khirwar, Ankur Narang
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 machine learning-based solution is introduced to predict surface-level nitrogen dioxide (NO2) concentrations using Sentinel-5P satellite imagery. The GeoFormer model combines vision transformer modules with time-series transformer modules to achieve high accuracy (MAE 5.65). This approach has the potential to advance climate change monitoring and emission regulation efforts globally. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Air pollution is a big problem that affects our health and the planet. Scientists are working on a new way to predict where pollutants are coming from using satellite images. They created a special model called GeoFormer that uses artificial intelligence to do this. It looks at pictures of the Earth taken by satellites and predicts where there will be high levels of pollution. This can help us understand how bad air pollution is and make decisions to reduce it. |
Keywords
* Artificial intelligence * Machine learning * Mae * Time series * Transformer * Vision transformer
