Improving Artificial-Intelligence-Based Individual Tree Species Classification Using Pseudo Tree Crown Derived from Unmanned Aerial Vehicle Imagery

被引:2
|
作者
Miao, Shengjie [1 ]
Zhang, Kongwen [2 ]
Zeng, Hongda [1 ]
Liu, Jane [1 ,3 ]
机构
[1] Fujian Normal Univ, Sch Geog Sci, Key Lab Humid Subtrop Ecogeog Proc, Minist Educ, Cangsan Campus, Fuzhou 350007, Peoples R China
[2] Univ Fraser Valley, Sch Comp, Abbotsford, BC V2S 7M7, Canada
[3] Univ Toronto, Dept Geog & Planning, Toronto, ON M5S 3G3, Canada
关键词
pseudo tree crown (PTC); deep learning (DL); machine learning (ML); artificial intelligence (AI); unmanned aerial vehicle (UAV); individual tree species (ITS) classification;
D O I
10.3390/rs16111849
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Urban tree classification enables informed decision-making processes in urban planning and management. This paper introduces a novel data reformation method, pseudo tree crown (PTC), which enhances the feature difference in the input layer and results in the improvement of the accuracy and efficiency of urban tree classification by utilizing artificial intelligence (AI) techniques. The study involved a comparative analysis of the performance of various machine learning (ML) classifiers. The results revealed a significant enhancement in classification accuracy, with an improvement exceeding 10% observed when high spatial resolution imagery captured by an unmanned aerial vehicle (UAV) was utilized. Furthermore, the study found an impressive average classification accuracy of 93% achieved by a classifier built on the PyTorch framework, with ResNet50 leveraged as its convolutional neural network layer. These findings underscore the potential of AI-driven approaches in advancing urban tree classification methodologies for enhanced urban planning and management practices.
引用
收藏
页数:21
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