Abstract
The increasing availability of Earth observation data offers unprecedented opportunities for large-scale environmental monitoring and analysis. However, these datasets are inherently heterogeneous, stemming from diverse sensors, geographical regions, acquisition times, and atmospheric conditions. Distribution shifts between training and deployment domains severely limit the generalization of pretrained remote sensing models, making unsupervised domain adaptation (UDA) crucial for real-world applications. We introduce FlowEO, a novel framework that leverages generative models for image-space UDA in Earth observation. We leverage flow matching to learn a semantically preserving mapping that transports from the source to the target image distribution. This allows us to tackle challenging domain adaptation configurations for classification and semantic segmentation of Earth observation images. We conduct extensive experiments across four datasets covering adaptation scenarios such as SAR to optical translation and temporal and semantic shifts caused by natural disasters. Experimental results demonstrate that FlowEO outperforms existing image translation approaches for domain adaptation while achieving on-par or better perceptual image quality, highlighting the potential of flow-matching-based UDA for remote sensing.
Abstract (translated)
地球观测数据的日益增多为大规模环境监测和分析提供了前所未有的机遇。然而，这些数据集本质上是异构的，源于不同的传感器、地理区域、获取时间和大气条件。训练域与部署域之间的分布变化极大地限制了预训练遥感模型的泛化能力，使得无监督领域适应（UDA）对于实际应用至关重要。我们引入了一个新的框架FlowEO，该框架利用生成模型来进行地球观测中的图像空间UDA。通过流匹配来学习一种保持语义的映射方法，将源域图像分布迁移到目标域图像分布。这使我们能够应对地球观测图像分类和语义分割中更具挑战性的领域适应配置。 我们在四个数据集上进行了广泛的实验，涵盖了从合成孔径雷达（SAR）到光学传感器转换以及由自然灾害引起的时间和语义变化等适应场景。实验结果表明，FlowEO在领域适应方面超过了现有的图像翻译方法，并且达到了同等或更好的感知图像质量，突显了基于流匹配的UDA对于遥感应用的巨大潜力。
URL
https://arxiv.org/abs/2512.05140
