Datasets Comparison

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.

Karabuk Universitesi

Image Classification, Forest Fire, Fire Detection

Creative Commons Attribution 4.0 International

The "Forest Fire Dataset" is a comprehensive and meticulously curated resource, specifically designed to support the development of algorithms for forest fire detection and object detection tasks. The dataset consists of 2,974 images dedicated to fire classification, which are divided into two primary categories: the first category includes images documenting active forest fires, while the second category contains images of intact, fire-free forest environments. This clear distinction within the dataset is crucial for training models to accurately differentiate between fire-affected and unaffected areas in forested regions. In addition to the fire classification data, the dataset includes 1,690 images dedicated to object detection, enhancing its applicability in machine learning and computer vision research. The dataset is carefully structured with a thoughtful distribution across training, validation, and test sets, with proportions of 80%, 15%, and 5%, respectively, to ensure that models trained on this data can generalize effectively to new, unseen data. The data were collected from various online sources and underwent rigorous manual filtering to maintain high data integrity. Additionally, a portion of the dataset was generated through controlled simulations of forest fires, conducted after obtaining the necessary approvals from relevant authorities. This simulated portion adds diversity and reliability to the dataset, providing a more comprehensive training ground for algorithms. By integrating both real-world and simulated scenarios, the "Forest Fire Dataset" offers a robust foundation for developing advanced fire detection systems, significantly contributing to forest conservation and disaster management efforts. For scientific research and advanced applications in the fields of forest fire detection and computer vision, the "Forest Fire Dataset" is a valuable tool. Researchers and practitioners are encouraged to refer to the published article that details the development of the system based on this dataset. To cite the article related to this dataset, the following citation can be used: ======================================================= I. Shamta and B. E. Demir, “Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV,” ِ Artica: PLoS One, vol. 19, no. 3, p. e0299058, 2024. ======================================================= Link to Article: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299058 ORCID: https://orcid.org/my-orcid?orcid=0009-0003-1280-679X Google Academik: https://scholar.google.com/citations?user=xP6CvtQAAAAJ&hl=tr

Karabuk Universitesi

Image Classification, Forest Fire, Fire Detection

Creative Commons Attribution 4.0 International