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

翻译：高质量地球观测（EO）图像的稀缺性构成了重大挑战，尽管其在各领域实现精确分析和支持明智决策中发挥着关键作用。这种稀缺性主要源于大气条件、季节变化和有限的地理覆盖范围，这使人工智能（AI）在地球观测中的应用变得复杂。数据增强是AI中广泛使用的技术，主要通过参数化图像变换生成额外数据，已被用于提升数据量和多样性。然而，该方法通常难以在关键语义轴上生成足够的多样性，从而对地球观测应用的准确性产生不利影响。为解决这一问题，我们提出了一种新颖的四阶段方法，旨在通过整合扩散模型来提升增强数据的多样性。我们的方法采用元提示进行指令生成，利用通用视觉-语言模型生成丰富的描述，微调地球观测扩散模型，并迭代式地增强数据。我们使用四种不同数据增强技术进行了广泛实验，所提方法始终展现出改进效果，超越现有增强方法，证明了其在生成语义丰富且多样化的地球观测图像方面的有效性。