Forest Fire Dataset
Description
This dataset comprises information related to forest fires and is intended for training algorithms designed for forest fire detection, alongside data for object detection. The section dedicated to fire classification consists of 2974 images, divided into two categories: the first category includes images depicting forest fires, while the second category contains images of intact forests without fires. As for the object detection data, it encompasses 1690 images, suitable for object detection purposes. These data have been distributed across training, validation, and test sets with proportions of 80%, 15%, and 5%, respectively. [1] A. Khan and B. Hassan, “Dataset for forest fire detection,” Mendeley Data, vol. 1, p. 2020, 2020, Accessed: Nov. 18, 2023. [Online]. Available: https://data.mendeley.com/datasets/gjmr63rz2r/1 [2] https://www.kaggle.com/datasets/phylake1337/fire-dataset These data were gathered from various sources on the internet and were manually filtered to ensure data integrity. Additionally, a portion of this data was generated manually by simulating forest fires after obtaining the necessary approvals from relevant authorities. This project is part of a master's thesis titled "Development of a Deep Learning-Based Surveillance System for Forest Fire Detection and Monitoring using UAV (İHA KULLANILARAK ORMAN YANGINLARININ TESPİTİ VE GÖRÜNTÜLENMESİ İÇİN DERİN ÖĞRENME TABANLI GÖZETLEME SİSTEMİNİN GELİŞTİRİLMESİ)" at Karabuk University in Turkey, conducted by the student Ibrahim Shmata and supervised by Dr. Batıkan Erdem Demir.
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Steps to reproduce
This research was supported by the Karabuk University within the scope of Scientific Research Projects under Grant No. [KBUBAP-23-YL-055].