Improving SAM 2 for Agricultural Land Segmentation through Fine-Tuning, Point Prompt Augmentation, and Negative Prompt Calibration - Yayang Setia Budi; Fardan Al Jihad; Nurjannah Syakrani; Trisna Gelar; Muhammad Rizqi Sholahuddin; Djoko Cahyo Utomo Lieharyani (Universitas Airlangga, 2026)
Background: The Segment Anything Model 2 (SAM 2) represents a state-of-the-art foundation model for object segmentation; however, its application to satellite-based agricultural mapping faces significant challenges. Standard SAM 2 architectures often struggle with the spectral ambiguity of fragmented tropical landscapes and the domain gap inherent in remote sensing imagery. Furthermore, the model’s interactive nature requires precise spatial guidance, making it sensitive to both the location and density of input prompts, which limits its scalability for automated large-scale monitoring. Objective: This study aims to (1) analyze the impact of domain-specific fine-tuning combined with automated Point Prompt Augmentation (PPA) and Negative Prompt Calibration (NPC) on segmentation accuracy; (2) evaluate the performance of four SAM 2 variants (Tiny, Small, Base+, and Large) to identify the optimal backbone for agricultural tasks; and (3) determine the optimal prompt density for both positive and negative points. Methods: The SAM 2 variants were fine-tuned using the LoveDA satellite dataset. Evaluation was conducted through an automated pipeline comparing two initialization strategies: Largest Agricultural Area (LAA) Centroid and random placement. The study implemented PPA to strategically increase positive prompt density and NPC to suppress