  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.
