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 imagery poses a significant challenge%2C despite its critical role in enabling precise analysis and informed decision-making across various sectors. This scarcity is primarily due to atmospheric conditions%2C seasonal variations%2C and limited geographical coverage%2C which complicates the application of Artificial Intelligence (AI) in EO. Data augmentation%2C a widely used technique in AI that involves generating additional data mainly through parameterized image transformations%2C has been employed to increase the volume and diversity of data. However%2C this method often falls short in generating sufficient diversity across key semantic axes%2C adversely affecting the accuracy of EO applications. To address this issue%2C we propose a novel four-stage approach aimed at improving the diversity of augmented data by integrating diffusion models. Our approach employs meta-prompts for instruction generation%2C harnesses general-purpose vision-language models for generating rich captions%2C fine-tunes an Earth Observation diffusion model%2C and iteratively augments data. We conducted extensive experiments using four different data augmentation techniques%2C and our approach consistently demonstrated improvements%2C outperforming the established augmentation methods%2C revealing its effectiveness in generating semantically rich and diverse EO images.?quality=80&w=800)
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