Summary of Carbonsense: a Multimodal Dataset and Baseline For Carbon Flux Modelling, by Matthew Fortier and Mats L. Richter and Oliver Sonnentag and Chris Pal
CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling
by Matthew Fortier, Mats L. Richter, Oliver Sonnentag, Chris Pal
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 machine learning-ready dataset is presented to promote comparisons between data-driven carbon flux modeling (DDCFM) approaches, which predict carbon fluxes from biophysical data. CarbonSense integrates measured carbon fluxes, meteorological predictors, and satellite imagery from 385 locations globally, facilitating robust model training. A baseline model using a current state-of-the-art DDCFM approach and a novel transformer-based model are also provided. Experiments illustrate the potential gains of multimodal deep learning techniques in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CarbonSense is a new dataset that helps scientists predict how much carbon dioxide our planet absorbs from the air. This information is important because it shows us if our efforts to reduce pollution are working or not. The dataset combines lots of different types of data, like measurements of carbon levels and weather forecasts, with pictures taken from space. This makes it easier for computers to learn about patterns in this data and make good predictions. Scientists can use these predictions to develop new models that help us understand our planet’s health better. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Transformer
