Summary of Generating Synthetic Satellite Imagery For Rare Objects: An Empirical Comparison Of Models and Metrics, by Tuong Vy Nguyen et al.
Generating Synthetic Satellite Imagery for Rare Objects: An Empirical Comparison of Models and Metrics
by Tuong Vy Nguyen, Johannes Hoster, Alexander Glaser, Kristian Hildebrand, Felix Biessmann
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); 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 A novel large-scale empirical evaluation of generative deep learning architectures is presented, focusing on generating synthetic satellite imagery for rare object categories like nuclear power plants. The study fine-tunes generative models to produce realistic images conditioned on textual input or detailed building layouts from a game engine. The generated images are assessed using common metrics and compared to human judgments from user studies to evaluate their trustworthiness. Results show that authentic synthetic satellite imagery is feasible for rare objects, but there is often a discrepancy between automated metrics and human perception, with strong negative correlations found. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative deep learning models can create realistic fake images, which could have big implications for society. One question is: How easy is it to make realistic images, especially for specific topics like satellite imagery? The process of getting the right image content is hard to automate and control. It’s also tricky to know if generated images are realistic or not. In this study, researchers tested large-scale generative models to generate synthetic satellite images of rare objects, like nuclear power plants. They used two types of input: text and detailed building layouts from a game engine. The generated images were evaluated using common metrics and compared to human opinions from user studies. The results show that it’s possible to create realistic synthetic satellite images for rare objects, but there can be a big difference between what machines think is good quality and what humans think is trustworthy. |
Keywords
» Artificial intelligence » Deep learning
