Improving SAM 2 for Agricultural Land Segmentation through Fine-Tuning, Point Prompt Augmentation, and Negative Prompt Calibration

Stratified Evaluation of SAM 2 for Zero-Shot Building Segmentation in Aerial Imagery

The first systematic zero-shot evaluation of SAM 2 for aerial building segmentation is presented, establishing SAM 2 as a viable tool for rapid building mapping while highlighting where domain adaptation remains necessary.

Rcl-sam: a parameter-efficient segment anything model for high-resolution remote sensing cultivated land extraction

AB-SAM: A SAM-Based Asymmetric Boundary-Aware Model for the Semantic Segmentation of Small and Medium-Sized Landslides

The results demonstrate that AB-SAM provides a practical parameter-efficient framework for automated, hint-free landslide segmentation, although further evaluation across additional regions, sensors, and landslide-size distributions remains necessary.

Manual Annotations Meet Fine-Tuned Foundation Models: A Comparison on Tree Crown Segmentation Task

Abstract. Accurate segmentation of individual tree crowns (ITCs) from remote-sensing imagery is essential for forest monitoring and ecological analysis, yet remains challenging due to overlapping canopies and structural variability. The Segment Anything Model (SAM) shows strong generalization capabilities but requires effective prompting and domain adaptation for remote sensing applications. In this study, we investigate a lightweight fine-tuning strategy using Low-Rank Adaptation (LoRA) to adapt SAM for ITC segmentation on the BAMFORESTS dataset. The impact of different prompting strategies is evaluated, including manually annotated point and bounding box prompts, as well as automatically generated bounding boxes derived from a pre-trained tree detector. SAM is fine-tuned with instance-level ITC masks, enabling prompt-aware segmentation of multiple tree crowns per image. Performance is assessed before and after fine-tuning using standard instance segmentation metrics, including IoU and F1-score. Results show that LoRA-based adaptation improves mask delineation and robustness to prompt variability, with bounding box prompts consistently outperforming point-based inputs. Automatically generated prompts enable a fully automated workflow, although their effectiveness depends on detection quality. Evaluation on an independent validation site with manually annotated ITC labels shows that the fine-tuned LoRA-SAM model achieves performance comparable to manual annotations, while significantly reducing annotation effort. These findings highlight the importance of prompt design in adapting foundation models for remote sensing tasks and demonstrate that parameter-efficient fine-tuning provides a practical pathway toward scalable ITC segmentation.

Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery

Spatio-temporal PV data are essential for understanding adoption processes in off-grid regions, yet such data remain largely unavailable. Automated segmentation of remote sensing (RS) imagery offers a promising solution; yet, residential PV systems remain challenging targets because of their small size and sparse distribution, resulting in severe target-background imbalance. Vision-language foundation models (FMs) provide a data-efficient paradigm through prompt-based semantic and spatial guidance, but the relative contribution of different prompt types remains unclear. We systematically evaluate SAM3 for small-scale PV segmentation in RS imagery by comparing textual, geometric, and hybrid prompting, under varying supervision levels, training strategies, spatial resolutions, and imaging conditions. Multi-temporal aerial imagery from a large off-grid rural region serves as a study site, with findings validated across three additional datasets. Prompting strategy emerged as the dominant factor governing model behavior. Textual prompting consistently produced the lowest performance and showed the greatest sensitivity to supervision and imaging conditions. In contrast, spatial guidance substantially improved both segmentation accuracy and robustness. Hybrid prompting achieved the highest accuracy and stability, indicating that semantic and spatial guidance provide complementary information. Most performance gains were achieved with only a few hundred annotated samples, demonstrating strong data efficiency. Transfer learning had limited overall impact, with only modest improvements observed for textual prompting under limited supervision. Overall, our findings establish prompting strategy as a key determinant of SAM3 adaptation, robustness, and generalization, highlighting the potential of promptable FMs for scalable PV mapping in data-constrained off-grid regions.

Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation

Remote sensing image semantic segmentation (RSISS) has attracted significant attention due to the growing demand for fine-grained land cover information. The Segment Anything Model (SAM), proposed as a foundation vision model, offers strong segmentation performance and generalization capabilities for RSISS tasks. However, existing SAM-based approaches face two limitations: (1) Insufficient adaptation of SAM's features to the diverse characteristics of land cover types. (2) Semantic ambiguity at object boundaries, which hinders accurate delineation. To address these limitations, we propose Frequency and Edge-guided SAM (FE-SAM), a scalable and efficient framework for RSISS. Specifically, we introduce a Frequency-Modulated Adapter (FMA) that adaptively decomposes and modulates frequency-domain features based on the input data. It selectively enhances informative high- and low-frequency components corresponding to different land cover types. Furthermore, to improve SAM's ability to capture fine-grained details, we design EGRefiner, which integrates multi-scale edge-enhanced information extracted from the input image. Extensive experiments on three benchmark datasets demonstrate that FE-SAM outperforms state-of-the-art methods. The source codes are available at: https://github.com/oucailab/FE-SAM.