SAM4CH4: Zero-Shot Methane Plume Mapping With Segment Anything and Vision-Language Models

Recent advances in foundation models, including large language models (LLMs) and advanced computer vision techniques, have opened new possibilities in remote sensing applications. One such model is the Segment Anything Model (SAM), which can perform image segmentation without task-specific training data. This is especially useful for detecting methane plumes in satellite imagery, where it is important to accurately separate methane column enhancements from complex background conditions. SAM's prompt-based segmentation approach helps address these challenges and reduces the need for large annotated datasets. In this study, we introduce SAM4CH4, a zero-shot segmentation framework that applies SAM for methane plume detection using Sentinel-2 imagery, with segmentation prompts automatically generated by text encoder models including Contrastive Language-Image Pre-training (CLIP), CLIP Surgery, and Grounding DINO. We evaluate the approach on both a synthetically generated benchmark dataset and real Sentinel-2 images. Results show that bounding-box prompts from the Swin-L variant of Grounding DINO, combined with the latest version of SAM (SAM2), consistently achieve high accuracy—exceeding 72% in F1-score and 95% in overall accuracy—and outperform a widely used statistical thresholding method by approximately 15% in F1-score. These results are also competitive with supervised deep learning methods, which typically require large labeled datasets and significant computational resources. By leveraging pre-trained models and removing the need for manual annotation, the proposed SAM4CH4 framework offers a zero-shot scalable and efficient solution for operational methane plume detection and monitoring.

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