This study proposes a zero-shot burned area mapping approach based on the Segment Anything Model (SAM) using Sentinel-2 data and demonstrates that SAM can serve as a powerful, scalable, and low-cost framework for zero-shot environmental monitoring and automatic burned area detection, particularly in data-scarce or time-critical post-fire assessment scenarios.
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.
Melih Altay, Fatih Fehmi Şi̇mşek, S. Abdikan· The International Archives o...· 0 citations
The results establish zero-shot NAS as a computationally efficient paradigm for large-scale Earth observation segmentation as a training-free strategy for semantic segmentation in Earth observation.
Gabriel Iuhasz, Marian Neagul· IEEE Access· 0 citations
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.
Bingning Xiong, Mingyu Ou· Journal of image processing...· 0 citations
We propose a vision transformer (ViT)-based deep learning framework to improve disaster-affected area segmentation from satellite images, supporting the Emergent Value Added Product (EVAP) system developed by the Taiwan Space Agency (TASA). The process begins with a small number of manually labeled regions. We then use principal component analysis (PCA) to expand these labels with a confidence interval, creating a weakly supervised training set. Our model, which takes multi-band input from Sentinel-2 and Formosat-5 satellites, is trained to distinguish disaster-affected areas using these expanded labels. We adopt several strategies to increase accuracy when only limited supervision is available. To evaluate performance, our predictions are compared to higher-resolution EVAP results to measure spatial accuracy and consistency. Experiments on the 2022 Poyang Lake drought and the 2023 Rhodes wildfire demonstrate smoother and more reliable delineations. Quantitative evaluation is conducted against manually refined ground truth provided by the Taiwan Space Agency (TASA), with EVAP baseline reported as an operational baseline for comparison.
Precise mapping of water bodies is crucial for flood monitoring, disaster risk and response reduction, as well as sustainable water resource management. In this paper, we introduce a deep learning model for effective segmentation of rivers, lakes, and reservoirs from high-resolution Gaofen-2 satellite images. Leveraging the Five-Billion-Pixels dataset-more than 5 billion annotated pixels for 24 land cover classes—our approach solves the problem of segmenting water bodies on various terrains and environmental conditions. The proposed U-Net and ViT-UNet models, with the former employing Vision Transformers to enhance global context perception. For enhancing generalization, the dataset is augmented using Albumentations and flipping, rotation, and scaling transformations. Hybrid loss functions of Dice Loss, Binary Cross-Entropy, and Focal Loss are employed to handle class imbalance, especially for slender river segments. The ViT-UNet model attained 98.8% pixel accuracy, which mirrors its ability to preserve fine detail and large-scale spatial pattern. Mixed-precision training and the AdamW optimizer has enhanced the computational efficiency. Further, demonstrates the potential of transformer-based segmentation models for remote sensing achieved accuracy of 98% for environmental risk management and decision support in disaster-prone areas.
T. S. Murthy, K. Rao, Swathi Sowmya Bavirthi· International Journal of Eng...· 0 citations
The automated detection and classification of surface degradation on historical buildings represents a critical challenge in architectural heritage conservation. Conventional approaches relying on manual inspection or supervised machine learning require extensive annotated datasets and expert involvement, limiting their scalability. This paper presents a novel zero-shot pipeline for degradation segmentation on historical civil architecture, combining UAV-acquired photogrammetric data processed in Agisoft Metashape with Gemma 4 31B, Google DeepMind's flagship open-weight vision language model, running locally via LM Studio, and the Segment Anything Model 2 (SAM2) for pixel-accurate mask generation. The system operates entirely without task-specific training data, producing segmentation masks overlaid on the RGB orthomosaic for expert visual evaluation. A case study on a degraded historical building in Calabria, southern Italy, demonstrates the pipeline's ability to detect and categorize detachment, cracking, and lacunae in a unified, reproducible workflow. Results are evaluated through structured expert visual assessment. The approach offers a replicable, low-cost alternative to supervised segmentation, particularly suited to contexts where labeled data is unavailable.
Francesco Demarco, Federico De Francesca, Pierpaolo Antonio Fusaro et al.· 2026 IEEE International Work...· 0 citations
