The paper investigates the potential and risks associated with using state-of-the-art text-to-image generation models, such as Stable Diffusion, to create synthetic satellite imagery. Key highlights:
The authors explore the technical requirements to adapt these models for generating synthetic satellite images, including fine-tuning on datasets of nuclear power plants and the UC Merced land-use dataset.
They evaluate the quality of the generated images using established metrics like Inception Score and Fréchet Inception Distance, as well as metrics adapted for remote sensing data.
The results show that while the synthetic images are not on par with real data, they can obtain evaluation scores of the same order of magnitude and can partly fool human evaluators in user studies.
The development of suitable evaluation metrics for synthetic image quality is an open research challenge. Existing metrics may not yield robust and reliable estimates, especially when limited real data is available for calibration.
The authors discuss the ethical and societal implications of these technologies, as the ease of generating large-scale synthetic datasets raises concerns about the potential for malicious use and the spread of misinformation.
They argue that the development of new generative approaches should be accompanied by better methods for detecting synthetic imagery, particularly in the remote sensing domain.
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Önemli Bilgiler Şuradan Elde Edildi
by Tuong Vy Ngu... : arxiv.org 04-12-2024
https://arxiv.org/pdf/2404.07754.pdfDaha Derin Sorular