fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model

TL;DR

fabSAM is a novel farmland boundary delineation method that enhances the Segment Anything Model with fine-tuning and a Deeplabv3+ based Prompter, significantly improving boundary detection accuracy in satellite imagery.

Contribution

The paper introduces fabSAM, a new framework combining SAM with fine-tuning and a Deeplabv3+ Prompter for improved farmland boundary delineation from remote sensing data.

Findings

fabSAM outperforms zero shot SAM by 23.5% and 15.1% in mIOU.

fabSAM surpasses Deeplabv3+ by 4.9% and 12.5% in mIOU.

fabSAM enables more accurate global farmland mapping from open satellite datasets.

Abstract

Delineating farmland boundaries is essential for agricultural management such as crop monitoring and agricultural census. Traditional methods using remote sensing imagery have been efficient but limited in generalisation. The Segment Anything Model (SAM), known for its impressive zero shot performance, has been adapted for remote sensing tasks through prompt learning and fine tuning. Here, we propose a SAM based farmland boundary delineation framework 'fabSAM' that combines a Deeplabv3+ based Prompter and SAM. Also, a fine tuning strategy was introduced to enable SAMs decoder to improve the use of prompt information. Experimental results on the AI4Boundaries and AI4SmallFarms datasets have shown that fabSAM has a significant improvement in farmland region identification and boundary delineation. Compared to zero shot SAM, fabSAM surpassed it by 23.5% and 15.1% in mIOU on the AI4Boundaries and AI4SmallFarms datasets, respectively. For Deeplabv3+, fabSAM outperformed it by 4.9% and 12.5% in mIOU, respectively. These results highlight the effectiveness of fabSAM, which also means that we can more easily obtain the global farmland region and boundary maps from open source satellite image datasets like Sentinel2.