Deep Learning-Driven Wildfire Detection in Türkiye Using Sentinel-2 Imagery and Convolutional Neural Networks
Yazarlar (6)
Prof. Dr. İsmail Rakıp KARAŞ Karabük Üniversitesi, Türkiye
Öğr. Gör. Hacer Kübra SEVİNÇ Karabük Üniversitesi, Türkiye
Huda Ibrahim
Universiti Utara Malaysia, Malezya
Universiti Utara Malaysia, Malezya
Mahmoud Mohamed
Karabük Üniversitesi, Türkiye
Karabük Üniversitesi, Türkiye
Arş. Gör. Sefa Şahin Karabük Üniversitesi, Türkiye
Mahamat Oumar Oumate Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | ||
| Dergi ISSN | 1682-1750 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | Scopus | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2026 |
| Cilt / Sayı / Sayfa | 48 / 4 / 179–185 | DOI | 10.5194/isprs-archives-XLVIII-4-W18-2025-179-2026 |
| Makale Linki | https://doi.org/10.5194/isprs-archives-xlviii-4-w18-2025-179-2026 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
Bilgisayar Yazılımı ve Yazılım Mühendisliği
Bilgi Sistemleri
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| Özet |
| Wildfires, intensified by climate change and anthropogenic factors, represent a major threat to ecosystems, infrastructure, and human life. Early detection is critical for effective prevention and mitigation. This study presents a Convolutional Neural Network (CNN)-based system designed to detect wildfires from Sentinel-2 satellite imagery. The methodology integrates advanced preprocessing techniques—including atmospheric correction, cloud masking, and spatial normalization—with a tailored CNN architecture optimized for spectral–spatial feature extraction. Training utilized a curated dataset of wildfire events in Türkiye (2016–2025), annotated at the pixel level to distinguish fire-affected and unaffected regions. The CNN model, employing LeakyReLU activation and AdamW optimization, achieved robust performance, with precision, recall, and F1-scores all averaging 0.97, and an overall accuracy of 97%. High fire-class precision (1.00) minimized false alarms, while strong recall (0.93) ensured reliable detection. The model’s lightweight design allows deployment on cloud platforms and edge devices, enabling real-time monitoring across forestry, agriculture, and urban planning applications. Analysis of Turkish wildfire statistics (1988–2023) revealed that over 90% of fires stem from human negligence or arson, underscoring the need for public education, stricter enforcement, and technology-driven surveillance. Long-term data further indicate a rising severity in fire events, strengthening the case for AI-enhanced early warning systems. This research demonstrates that CNNs applied to Sentinel-2 imagery offer a scalable, accurate, and cost … |
| Anahtar Kelimeler |
| Convolutional Neural Network (CNN) | Deep Learning | Remote Sensing | Sentinel-2 Imagery | Wildfire Detection |
BM Sürdürülebilir Kalkınma Amaçları
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