Summary of An Open and Large-scale Dataset For Multi-modal Climate Change-aware Crop Yield Predictions, by Fudong Lin et al.
An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions
by Fudong Lin, Kaleb Guillot, Summer Crawford, Yihe Zhang, Xu Yuan, Nian-Feng Tzeng
First submitted to arxiv on: 10 Jun 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 The proposed CropNet dataset is a terabyte-sized, publicly available, and multi-modal repository specifically designed for predicting crop yields at the county level. This dataset aims to facilitate researchers in developing deep learning models that account for both short-term weather variations and long-term climate change effects on crop yields. The dataset comprises three modalities of data: Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, covering over 2200 US counties spanning six years (2017-2022). By leveraging this dataset, researchers can build versatile deep learning models for timely and precise crop yield predictions. The CropNet package offers three types of APIs to facilitate data downloading and model building. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The CropNet dataset is a big deal because it helps us predict how much food we’ll grow each year. This is important because we need to make sure we have enough food, especially as the weather gets warmer due to climate change. The dataset contains lots of information about different parts of the country and what the weather was like from 2017 to 2022. It’s like a big puzzle piece that helps us figure out how to grow more food in a way that’s good for the planet. |
Keywords
» Artificial intelligence » Deep learning » Multi modal
