An innovative hybrid method utilizing fused transformer-based deep features and deep neural networks for detecting forest fires
Yazarlar (1)
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Advances in Space Research (Q1) | ||
| Dergi ISSN | 0273-1177 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2025 |
| Cilt / Sayı / Sayfa | 75 / 12 / 8583–8598 | DOI | 10.1016/j.asr.2025.04.020 |
| Makale Linki | https://doi.org/10.1016/j.asr.2025.04.020 | ||
| Özet |
| Forest fires, one of the most pernicious and devastating disasters, cause deforestation, wildlife extinction, global warming, and climate change. Early fire detection is critical before it reaches catastrophic dimensions. Artificial intelligence-based systems that detect forest fires accurately and quickly are needed for early intervention. Delayed extinguishing efforts without such systems cause tremendous damage and losses. An effective monitoring system allows for the reduction of fire damage and hence prevents forest loss. This study aims to develop a successful artificial intelligence model to detect fire from forest landscape images. In this context, this work is new as it provides new insights into robust and scientific modeling for forest fire detection by analyzing feature maps based on fused transformer architectures using Deep Neural Networks. The experimental models were validated using accuracy, sensitivity … |
| Anahtar Kelimeler |
| Computer vision | Deep neural networks | Forest fires | Transformer-based deep features |
Science Direct
An innovative hybrid method utilizing fused transformer-based deep features and deep neural networks for detecting forest fires
| Atıf Sayıları | |
| Scopus | 6 |
| Google Scholar | 7 |