Synthetic Satellite

Synthetic satellite imagery generation leverages deep learning, particularly generative adversarial networks and text-to-image models, to create realistic, high-resolution images for various applications. Current research focuses on improving the realism and controllability of these synthetic images, often using game engines and conditioning mechanisms (e.g., textual or mask-based) to guide the generation process and address the limitations of limited real-world datasets. This technology is proving valuable for augmenting training data in computer vision tasks like object detection (e.g., whale detection, satellite identification), supporting verification exercises, and potentially mitigating the scarcity of high-quality satellite imagery for open-source analysis. However, challenges remain in accurately assessing image fidelity and distinguishing synthetic from real imagery, highlighting the need for robust detection methods.