Mowla Md Najmul, Asadi Davood, Tekeoglu Kadriye Nur, Masum Shamsul, Rabie Khaled
Alparslan Türkeş Science and Technology University, Adana 1250, Turkey.
Hasan Kalyoncu University, Gaziantep 27100, Turkey.
Data Brief. 2024 Jul 3;55:110706. doi: 10.1016/j.dib.2024.110706. eCollection 2024 Aug.
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 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.
森林生态系统面临着日益增加的野火威胁，这就需要迅速而精确的检测方法来确保有效的火灾控制。然而，实时森林火灾数据的可获取性和及时性还有待提高。我们的研究通过引入基于无人机（UAVs）的森林火灾数据库（UAVs - FFDB）来应对这一挑战，该数据库具有双重构成。首先，它包含了1653张高分辨率RGB原始图像，这些图像是使用标准的S500四轴飞行器框架结合RaspiCamV2相机精心拍摄的。其次，该数据库纳入了增强数据，最终图像总数达到15560张，从而提高了数据集的多样性和全面性。这些图像是在土耳其阿达纳的阿达纳·阿尔帕斯兰·图尔克什科技大学附近的一片森林区域内拍摄的。数据集中的每张原始图像尺寸从353×314到640×480不等，而增强数据的尺寸范围是398×358到640×480，原始数据子集的数据集总大小为692MB。相比之下，增强数据子集的大小要大得多，总计6.76GB。原始图像是在一次无人机监测任务中获取的，相机精确地倾斜 - 180度与地面水平。图像拍摄的高度在5 - 15米之间交替，以多样化视野并建立一个更具包容性的数据库。在监测操作期间，无人机的平均速度为2。在此之后，使用先进的标注平台Makesense.ai对数据集进行了细致的标注，从而能够准确划分火灾边界。该资源为研究人员提供了必要的数据基础设施，以开发用于早期火灾检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实践。此外，UAVs - FFDB数据集是先进的基于人工智能的方法进步和完善的基础基石，旨在以无与伦比的精度和效率自动化火灾分类、识别、检测和分割任务。
