The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at https://github.com/ViTAE-Transformer/SAMRS.

翻译：Segment Anything Model (SAM) 的成功彰显了以数据为核心的机器学习的重要性。然而，由于遥感图像标注工作面临难度大、成本高等问题，大量有价值的遥感数据仍未被标注，尤其在像素级别的标注层面。本研究利用SAM与现有遥感目标检测数据集，构建了一条高效生成大规模遥感分割数据集的流水线，该数据集被命名为SAMRS。SAMRS共包含105,090张图像与1,668,241个实例，其规模超过现有高分辨率遥感分割数据集数个数量级。该数据集提供物体类别、位置及实例信息，可分别或组合用于语义分割、实例分割及目标检测任务。我们亦从多维度对SAMRS进行了全面分析。此外，初步实验表明，利用SAMRS开展分割预训练对于解决任务差异、缓解微调阶段训练数据不足带来的限制具有重要意义。相关代码与数据集将发布于https://github.com/ViTAE-Transformer/SAMRS。