森林生态系统面临越来越多的野火威胁,要求及时和精确的检测方法,以确保有效的消防控制。然而,森林火灾数据的实时可访问性和及时性要求提高。我们的研究通过引入基于无人机(UAV)的森林火灾数据库(UAV-FFDB)来解决这一挑战,以双重组成为特征。首先,它包含一组1653高分辨率RGB原始图像,这些图像是利用标准S500四轴飞行器框架与RaspiCamV2相机精心捕获的。其次,数据库包含增强的数据,最终共有15560张图像,从而增强数据集的多样性和全面性。这些图像是在阿达纳阿达纳·阿尔帕斯兰·蒂尔克什科学技术大学附近的森林区域内捕获的,土耳其。数据集中的每个原始图像的尺寸从353×314到640×480,而增强的数据范围从398x358到640×480,导致原始数据子集的总数据集大小为692MB。相比之下,增强的数据子集占了相当大的规模,总计6.76GB。原始图像是在无人机监视任务期间获得的,相机精确地成-180度的角度与地面水平。这些图像是从海拔5-15米之间交替拍摄的,以使视野多样化并建立更具包容性的数据库。在监视行动中,无人机平均速度为2米/秒。在此之后,使用高级注释平台对数据集进行了细致的注释,Makesense.ai,能够准确划分火灾边界。这一资源为研究人员提供了必要的数据基础设施,以开发早期火灾探测和持续监测的创新方法,加强保护生态系统和人类生命的努力,同时促进可持续森林管理做法。此外,UAV-FFDB数据集作为先进的基于人工智能的方法的进步和完善的基础基石,旨在自动化火灾分类,认可,检测,和分割任务具有无与伦比的精度和效率。
Forest ecosystems face increasing wildfire threats, demanding prompt and precise detection methods to ensure efficient fire control. However, real-time forest fire data accessibility and timeliness require improvement. Our study addresses the challenge through the introduction of the Unmanned Aerial Vehicles (UAVs) based forest fire database (UAVs-FFDB), characterized by a dual composition. Firstly, it encompasses a collection of 1653 high-resolution RGB raw images meticulously captured utilizing a standard S500 quadcopter frame in conjunction with a RaspiCamV2 camera. Secondly, the database incorporates augmented data, culminating in a total of 15560 images, thereby enhancing the diversity and comprehensiveness of the dataset. These images were captured within a forested area adjacent to Adana Alparslan Türkeş Science and Technology University in Adana, Turkey. Each raw image in the dataset spans dimensions from 353 × 314 to 640 × 480, while augmented data ranges from 398 × 358 to 640 × 480, resulting in a total dataset size of 692 MB for the raw data subset. In contrast, the augmented data subset accounts for a considerably larger size, totaling 6.76 GB. The raw images are obtained during a UAV surveillance mission, with the camera precisely angled a -180-degree to be horizontal to the ground. The images are taken from altitudes alternating between 5 - 15 meters to diversify the field of vision and to build a more inclusive database. During the surveillance operation, the UAV speed is 2 m/s on average. Following this, the dataset underwent meticulous annotation using the advanced annotation platform, Makesense.ai, enabling accurate demarcation of fire boundaries. This resource equips researchers with the necessary data infrastructure to develop innovative methodologies for early fire detection and continuous monitoring, enhancing efforts to protect ecosystems and human lives while promoting sustainable forest management practices. Additionally, the UAVs-FFDB dataset serves as a foundational cornerstone for the advancement and refinement of state-of-the-art AI-based methodologies, aiming to automate fire classification, recognition, detection, and segmentation tasks with unparalleled precision and efficacy.