Abstract. Accurate and timely land cover mapping in heterogeneous metropolitan environments remains a fundamental challenge in Earth observation, particularly under conditions where optical imagery is compromised by cloud cover or seasonal atmospheric interference. This study presents a systematic evaluation of four state-of-the-art machine learning algorithms Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a shallow Artificial Neural Network (ANN) for pixel-based land cover classification over the Istanbul metropolitan region using single-date ALOS-2 PALSAR-2 L-band Synthetic Aperture Radar (SAR) imagery. The methodological framework integrates dual-polarimetric backscatter coefficients (HH and HV) with Grey-Level Co-occurrence Matrix (GLCM) texture features, Land Parcel Identification System (LPIS) boundaries for reference data delineation, Bayesian hyperparameter optimization, and LightGBM-guided Recursive Feature Elimination (RFE) to establish a reproducible and computationally efficient classification pipeline. Among all tested configurations, LightGBM achieved the highest overall accuracy (OA = 85.1%, κ = 0.81) with a 10-feature subset identified through RFE, while XGBoost demonstrated the strongest performance for urban class discrimination. Bayesian optimization yielded statistically meaningful improvements over default configurations for all gradient-boosting models. The optimal feature count was found to be ten, with HV-derived texture features particularly Entropy and Contrast identified as the most discriminative predictors. These results confirm that systematic feature engineering and algorithm tuning are as critical as classifier selection in SAR-based land cover mapping and lay the foundation for scalable operational workflows applicable to rapidly urbanizing regions.
Melih Altay, B. Tavus, Fatih Fehmi Şi̇mşek et al.· The International Archives o...· 0 citations
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
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.