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 surpasses existing high-resolution RS segmentation datasets in size by several orders of magnitude, and 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. We hope it could facilitate research in RS segmentation, particularly in large model pre-training.

本研究提出SAMRS，通过整合SAM和现有的遥感物体检测数据集以生成大规模遥感分割数据集，该数据集在大小上超过了现有的高分辨率遥感分割数据集。SAMRS可用于语义分割、实例分割和目标检测等研究领域。同时，我们对SAMRS进行了全面的分析并希望它能促进遥感分割、特别是大规模模型预训练等方面的研究。

使用Segment Anything模型扩大遥感分割数据集的规模