SAM-Aug: Leveraging SAM Priors for Few-Shot Parcel Segmentation in Satellite Time Series

TL;DR

SAM-Aug introduces a novel framework that leverages the Segment Anything Model's priors to enhance few-shot land cover segmentation in satellite time series, significantly improving accuracy with minimal labeled data.

Contribution

The paper proposes SAM-Aug, a new annotation-efficient method that uses SAM-derived priors and a novel loss to improve few-shot remote sensing segmentation without additional annotations.

Findings

Outperforms state-of-the-art by +2.33 mIoU under 5% labels

Achieves up to 40.28% mIoU on the best seed

Demonstrates robustness across multiple random seeds

Abstract

Few-shot semantic segmentation of time-series remote sensing images remains a critical challenge, particularly in regions where labeled data is scarce or costly to obtain. While state-of-the-art models perform well under full supervision, their performance degrades significantly under limited labeling, limiting their real-world applicability. In this work, we propose SAM-Aug, a new annotation-efficient framework that leverages the geometry-aware segmentation capability of the Segment Anything Model (SAM) to improve few-shot land cover mapping. Our approach constructs cloud-free composite images from temporal sequences and applies SAM in a fully unsupervised manner to generate geometry-aware mask priors. These priors are then integrated into training through a proposed loss function called RegionSmoothLoss, which enforces prediction consistency within each SAM-derived region across temporal frames, effectively regularizing the model to respect semantically coherent structures. Extensive experiments on the PASTIS-R benchmark under a 5 percent labeled setting demonstrate the effectiveness and robustness of SAM-Aug. Averaged over three random seeds (42, 2025, 4090), our method achieves a mean test mIoU of 36.21 percent, outperforming the state-of-the-art baseline by +2.33 percentage points, a relative improvement of 6.89 percent. Notably, on the most favorable split (seed=42), SAM-Aug reaches a test mIoU of 40.28 percent, representing an 11.2 percent relative gain with no additional labeled data. The consistent improvement across all seeds confirms the generalization power of leveraging foundation model priors under annotation scarcity. Our results highlight that vision models like SAM can serve as useful regularizers in few-shot remote sensing learning, offering a scalable and plug-and-play solution for land cover monitoring without requiring manual annotations or model fine-tuning.