Forest Fire Dataset
Description
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
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Karabük University
KBUBAP-23-YL-055