Summary of Data Augmentation in Earth Observation: a Diffusion Model Approach, by Tiago Sousa et al.
Data Augmentation in Earth Observation: A Diffusion Model Approach
by Tiago Sousa, Benoît Ries, Nicolas Guelfi
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 addresses a critical challenge in Earth Observation (EO), where scarcity of high-quality imagery hinders precise analysis and informed decision-making. Artificial Intelligence (AI) applications are severely impacted due to limited data diversity. Data augmentation techniques, such as parameterized image transformations, have been employed to increase volume and diversity. However, these methods often fall short in generating sufficient semantic diversity, affecting EO application accuracy. To improve diversity, this paper proposes a novel four-stage approach integrating diffusion models for Earth Observation (EO) imagery. The approach involves meta-prompts, general-purpose vision-language models, fine-tuning an EO diffusion model, and iterative data augmentation. Experimental results demonstrate consistent improvements over established methods, highlighting the effectiveness of this approach in generating rich and diverse EO images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand the Earth without good pictures! That’s what scientists face when they try to analyze and make decisions about our planet. Right now, there just aren’t enough high-quality images to help them do their job well. To fix this problem, researchers are using something called “artificial intelligence” (AI) to help them create more images. But the current method of creating new images isn’t very good at making sure they’re accurate and varied. So, a team of scientists has come up with a new way to do things! They’re using special models that can generate lots of different images based on what they know about Earth. The results are really promising – their approach is better than the old method and will help us make more informed decisions about our planet. |
Keywords
» Artificial intelligence » Data augmentation » Diffusion model » Fine tuning
