Author

Melih Altay

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Open access Jul 2026

Leveraging PolSAR Features and Machine Learning for Improved Land Cover Discrimination with ALOS-2 PALSAR-2: A Comprehensive Evaluation over the Istanbul Metropolitan Region

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

Open access Jul 2026

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

A zero-shot burned-area mapping framework based on the Segment Anything Model (SAM) and multispectral Sentinel-2 imagery and integrating index-based composites with SAM outputs significantly enhances the discrimination between burned and unburned surfaces by reducing boundary fragmentation and spectral confusion in het...

Melih Altay, Fatih Fehmi Şi̇mşek, S. Abdikan · The International Archives o... · 0 citations

Jul 2026

Zero-shot burned area mapping with the Segment Anything Model (SAM): a label-free framework for post-fire environmental assessment

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...

Fatih Fehmi Şi̇mşek, Melih Altay · Environmental Monitoring & A... · 0 citations