UAVS-FDDB: UAVs-based Forest Fire Detection Database

Published: 10 May 2024| Version 2 | DOI: 10.17632/5m98kvdkyt.2


The UAVs-based Forest Fire Database (UAVs-FFDB) encompasses four distinct classes: 1. Pre-evening Forest Condition 2. Evening Forest Condition 3. Pre-evening Fire Incident 4. Evening Fire Incident. The images were captured using UAVs equipped with Raspberry Pi Camera V2 technology in the forested areas surrounding Adana Alparslan Türkeş Science and Technology University, Adana, Turkey. This dataset is divided into two main components: original data (raw) and augmented data, each accompanied by an annotation file. The raw data comprises 1,653 images, while the augmented dataset contains 15,560 images. Below is the distribution of images across the four classes: -Raw Data Pre-evening Forest Condition = 222 Evening Forest Condition = 286 Pre-evening Fire Incident = 791 Evening Fire Incident = 354 -Augmented Data Pre-evening Forest Condition = 3,890 Evening Forest Condition = 3,890 Pre-evening Fire Incident = 3,890 Evening Fire Incident = 3,890



Adana Bilim ve Teknoloji Universitesi


Ecology, Artificial Intelligence, Environmental Engineering, Image Acquisition, Machine Learning, Unmanned Aerial Vehicle (Space Vehicle), Image Classification, Forest Fire, Deep Learning, Database, Forestry Fire Management


Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 123M222. The authors thank TUBITAK for their support.