Bayberry Tree Recognition Dataset Based on the Aerial Photos and Deep Learning Model

WANG Dongliang1LUO Wei2

1 Key Laboratory of Land Surface Pattern and Simulation，Institute of Geographic Sciences and Natural Resources Research，Chinese Academy of Sciences，Beijing 100101，China2 Center for Intelligent Manufacturing Electronics，Institute of Microelectronics，Chinese Academy of sciences，Beijing 100029，China

DOI:10.3974/geodb.2019.04.16.V1

Published:Jul. 2019

Visitors：4223       Data Files Downloaded：6012      
Data Downloaded：2224037.89 MB      Citations：

Key Words：

Bayberry tree,recognition,aerial photos,Mask RCNN

Abstract：

Bayberry (Myrica rubra) is an evergreen tree with 5-15 meters high; the diameter of the tree is about 60 cm at breast height and a crown of more than 5 meters. Bayberry tree is widely distributed in the southern of the Yangtze River Basin of China. Bayberry tree grows in acid red soil below 1500 meters above sea level and sunny mountain slopes. Its fruit is a special fruit with geographical characteristics in the southern of the Yangtze River Basin. The authors used the DJI Phantom 4 unmanned aerial systems (UAS) to take the aerial photography in Dayangshan Forest Park, Yongjia County, Zhejiang Province from January 23 to 24, 2019. Polygonal markers of Bayberry trees were then labeled before using a deep learning model, Mask RCNN (Mask Region Convolutional Neural Networks), to automatic identify the Bayberry trees. The recognized results were validated by visual interpretation method. The experimental results show that Mask RCNN has a high accuracy in bayberry tree recognition. The overall true positive rate is 90.08%, the false positive rate is 9.62%, and the loss positive rate is 9.92%. The experimental dataset of the presented bayberry tree recognition depth learning model includes: (1) 3080 UAS images captured in Dayangshan Forest Park, Yongjia County, Zhejiang Province, with an image size of 5472 x 3648; (2) Marked bayberry tree samples, including 298 image tiles; and (3) bayberry tree recognition results by the deep learning model, including 14 image tiles. The dataset is archived in .jpg and .json data formats, consists of 3690 data files with data size of 25.6 GB (Compressed to 71 files with data size of 25.5 GB).

Foundation Item：

Chinese Academy of Sciences (XDA23100200); Ministry of Science and Technology of P. R. China (2017YFC0506505, 2017YFB0503005)

Data Citation：

WANG Dongliang, LUO Wei.Bayberry Tree Recognition Dataset Based on the Aerial Photos and Deep Learning Model[J/DB/OL]. Digital Journal of Global Change Data Repository, 2019. https://doi.org/10.3974/geodb.2019.04.16.V1.

Wang, D. L., Luo, W. Bayberry tree recognition dataset based on the aerial photos and deep learning model [J]. Journal of Global Change Data & Discovery, 2019, 3(3): 290–296. DOI: 10.3974/geodp.2019.03.10.

Data Product: