Summary of On the Generalizability Of Foundation Models For Crop Type Mapping, by Yi-chia Chang et al.
On the Generalizability of Foundation Models for Crop Type Mapping
by Yi-Chia Chang, Adam J. Stewart, Favyen Bastani, Piper Wolters, Shreya Kannan, George R. Huber, Jingtong Wang, Arindam Banerjee
First submitted to arxiv on: 14 Sep 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 The paper explores the transfer learning capabilities of foundation models pre-trained on self-supervised learning (SSL) for Earth observation (EO) applications in agriculture, such as precision farming and crop monitoring. Previous studies have shown that these models can excel in tasks like language understanding, text generation, and image recognition. However, there are concerns about geospatial bias, where models trained on data-rich regions might not generalize well to data-scarce areas. This research investigates the ability of popular EO foundation models to transfer learning to new geographic locations using five crop classification datasets from different continents. The study compares three foundation models pre-trained on SSL4EO-S12, SatlasPretrain, and ImageNet, evaluating their performance in both in-distribution (ID) and out-of-distribution (OOD) settings. The results show that models designed explicitly for Sentinel-2, such as SSL4EO-S12, outperform general pre-trained weights like ImageNet. Additionally, the study highlights the importance of adequate labeled images to achieve high overall accuracy, emphasizing the need for more data in certain regions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well foundation models can be used for agriculture and crop monitoring around the world. These models are trained using self-supervised learning (SSL) and have been shown to work well for tasks like image recognition. However, there’s a concern that these models might not work as well in areas with less data. The study tests three different foundation models on five datasets from different continents to see how well they can be used for crop classification. The results show that some models are better than others at transferring learning to new regions and that having enough labeled images is important. |
Keywords
» Artificial intelligence » Classification » Language understanding » Precision » Self supervised » Text generation » Transfer learning
