A Novel Label-Free Approach for Post-Fire Environmental Assessment Based on Zero-Shot Segment Anything Model (SAM)

Abstract. Accurate and timely burned-area delineation is essential for quantifying wildfire impacts on ecosystem functioning, carbon dynamics, and post-fire recovery. Conventional pixel-based approaches remain sensitive to spectral ambiguity, topographic effects, and empirically defined thresholds, while recent deep learning models (e.g., U-Net, DeepLab, SegFormer) are constrained by their dependence on large, site-specific labelled datasets and repeated regional retraining. This study proposes a zero-shot burned-area mapping framework based on the Segment Anything Model (SAM) and multispectral Sentinel-2 imagery. Composite representations derived from ΔNBR, ΔNBR2, and ΔNDVI were generated and used as primary inputs to SAM in a label-free configuration. The effects of alternative pre-processing strategies, post-processing operations, and key hyperparameter settings were systematically investigated. Results show that multi-scale inference (crop_n_layers = 2) substantially improves geometric consistency and boundary accuracy of the extracted burned-area masks. The highest Intersection over Union values reached 0.89 for the Bursa study site and 0.87 for the Çanakkale study site, with corresponding F1 scores of 0.94 and 0.92, respectively. Despite the complete absence of training samples, SAM achieves performance comparable to, and in some cases exceeding, that of supervised deep learning approaches. Furthermore, integrating index-based composites with SAM outputs significantly enhances the discrimination between burned and unburned surfaces by reducing boundary fragmentation and spectral confusion in heterogeneous landscapes. By eliminating the need for manually labeled training data, the proposed framework addresses a major operational bottleneck in deep learning–based remote sensing. Overall, the study demonstrates a fast, scalable, and cost-effective solution for operational burned-area mapping and highlights the strong potential of SAM for zero-shot environmental monitoring and rapid post-fire response.