  In the fast-growing field of Remote Sensing (RS) image analysis, the gap
between massive unlabeled datasets and the ability to fully utilize these
datasets for advanced RS analytics presents a significant challenge. To fill
the gap, our work introduces an innovative auto-labeling framework named ALPS
(Automatic Labeling for Pre-training in Segmentation), leveraging the Segment
Anything Model (SAM) to predict precise pseudo-labels for RS images without
necessitating prior annotations or additional prompts. The proposed pipeline
significantly reduces the labor and resource demands traditionally associated
with annotating RS datasets. By constructing two comprehensive pseudo-labeled
RS datasets via ALPS for pre-training purposes, our approach enhances the
performance of downstream tasks across various benchmarks, including iSAID and
ISPRS Potsdam. Experiments demonstrate the effectiveness of our framework,
showcasing its ability to generalize well across multiple tasks even under the
scarcity of extensively annotated datasets, offering a scalable solution to
automatic segmentation and annotation challenges in the field. In addition, the
proposed a pipeline is flexible and can be applied to medical image
segmentation, remarkably boosting the performance. Note that ALPS utilizes
pre-trained SAM to semi-automatically annotate RS images without additional
manual annotations. Though every component in the pipeline has bee well
explored, integrating clustering algorithms with SAM and novel pseudo-label
alignment significantly enhances RS segmentation, as an off-the-shelf tool for
pre-training data preparation. Our source code is available at:
https://github.com/StriveZs/ALPS.
