Abstract
Remote sensing image (RSI) interpretation typically faces challenges due to the scarcity of labeled data, which limits the performance of RSI interpretation tasks. To tackle this challenge, we propose EarthSynth, a diffusion-based generative foundation model that enables synthesizing multi-category, cross-satellite labeled Earth observation for downstream RSI interpretation tasks. To the best of our knowledge, EarthSynth is the first to explore multi-task generation for remote sensing. EarthSynth, trained on the EarthSynth-180K dataset, employs the Counterfactual Composition training strategy to improve training data diversity and enhance category control. Furthermore, a rule-based method of R-Filter is proposed to filter more informative synthetic data for downstream tasks. We evaluate our EarthSynth on scene classification, object detection, and semantic segmentation in open-world scenarios, offering a practical solution for advancing RSI interpretation.
Abstract (translated)
遥感图像（RSI）解释通常面临由于标注数据稀缺而带来的挑战，这限制了RSI解释任务的性能。为了应对这一挑战，我们提出了EarthSynth，这是一种基于扩散模型的生成式基础模型，能够为下游RSI解释任务合成多类别、跨卫星平台的地球观测数据标签。据我们所知，EarthSynth是第一个探索遥感中的多任务生成的研究。 EarthSynth在EarthSynth-180K数据集上进行训练，并采用反事实组合（Counterfactual Composition）的训练策略来提高训练数据多样性并增强类别控制能力。此外，还提出了一种基于规则的方法R-Filter，用于过滤出对下游任务更有信息量的人工合成数据。 我们在开放世界的场景分类、目标检测和语义分割中评估了我们的EarthSynth模型，并为推进RSI解释提供了切实可行的解决方案。
URL
https://arxiv.org/abs/2505.12108
