Istiak Md Abrar, Khan Razib Hayat, Rony Jahid Hasan, Syeed M M Mahbubul, Ashrafuzzaman M, Karim Md Rajaul, Hossain Md Shakhawat, Uddin Mohammad Faisal
RIoT Research Center, Independent University, Bangladesh, Dhaka, 1229, Bangladesh.
Department of Computer Science and Engineering, Independent University, Bangladesh, Dhaka, 1229, Bangladesh.
Sci Data. 2024 Dec 20;11(1):1411. doi: 10.1038/s41597-024-04155-6.
Aquatic vegetation species are declining gradually, posing a threat to the stability of aquatic ecosystems. The decline can be controlled with proper monitoring and mapping of the species for effective conservation and management. The Unmanned Ariel Vehicle (UAV) aka Drone can be deployed to comprehensively capture large area of water bodies for effective mapping and monitoring. This study developed the AqUavplant dataset consisting of 197 high resolution (3840px × 2160px, 4K) images of 31 aquatic plant species collected from nine different sites in Bangladesh. The DJI Mavic 3 Pro triple-camera professional drone is used with a ground sampling distance (GSD) value of 0.04-0.05 cm/px for optimal image collection without losing detail. The dataset is complemented with binary and multiclass semantic segmentation mask to facilitate ML based model development for automatic plant mapping. The dataset can be used to detect the diversity of indigenous and invasive species, monitor plant growth and diseases, measure the growth ratio to preserve biodiversity, and prevent extinction.
水生植被物种正在逐渐减少，这对水生生态系统的稳定性构成了威胁。通过对这些物种进行适当的监测和测绘，可以控制这种减少，以实现有效的保护和管理。可以部署无人机（无人驾驶飞行器，即UAV）来全面捕捉大面积水体，以进行有效的测绘和监测。本研究开发了AqUavplant数据集，该数据集由从孟加拉国九个不同地点收集的31种水生植物的197张高分辨率（3840px×2160px，4K）图像组成。使用大疆Mavic 3 Pro三摄像头专业无人机，地面采样距离（GSD）值为0.04 - 0.05厘米/像素，以在不损失细节的情况下实现最佳图像采集。该数据集辅以二进制和多类语义分割掩码，以促进基于机器学习的自动植物测绘模型开发。该数据集可用于检测本地物种和入侵物种的多样性、监测植物生长和病害、测量生长率以保护生物多样性以及防止物种灭绝。
