Wildfire Detection from Sentinel Imagery using Convolutional Neural Network (CNN)
Yazarlar (6)
Dr. Öğr. Üyesi Sohaib K.M. Abujayyab Karabük Üniversitesi, Türkiye
Prof. Dr. İsmail Rakıp KARAŞ Karabük Üniversitesi, Türkiye
Javad Hashempour International College of Engineering And Management, Umman
E. Emircan Karabük Üniversitesi, Türkiye
K. Orçun Karabük Üniversitesi, Türkiye
G. Ahmet Karabük Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | |||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1007/978-3-031-54376-0_31 | ||
| Kongre Adı | The 8th International Conference on Smart City Applications (SCA 2023) | ||
| Kongre Tarihi | 04-10-2023 / 06-10-2023 | ||
| Basıldığı Ülke | Fransa | Basıldığı Şehir | Paris |
| UAK Araştırma Alanları |
Bilgi Sistemleri
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| Özet |
| Wildfires are a significant threat to the environment and human life, and early detection is crucial for effective wildfire management. The aim of this research work is to develop a deep learning model using CNN method for detecting wildfire using satellite imagery. The CNN model development process involves splitting the dataset into training and testing sets, pre-processing the data using ImageDataGenerator function, building a deep learning model using Sequential function, compiling the model using Adam optimizer, categorical cross-entropy loss function, and accuracy metric, and training the model using the fit generator function. The CNN model architecture includes Conv2D, MaxPooling2D, and Dense layers.The dataset was collected from Sentinel-2 L1C, including 159 fire images and 149 non-fire images from different places in the Mediterranean region of Turkey. The CNN model was developed using the … |
| Anahtar Kelimeler |
| Convolutional Neural Network (CNN) | Satellite imagery | Sentinel-2 | Wildfire detection |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 2 |