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

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 11; p. 1849
• Main Authors: Miao, Shengjie, Zhang, Kongwen (Frank), Zeng, Hongda, Liu, Jane
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; artificial intelligence (AI); Artificial neural networks; Classification; Classifiers; Comparative analysis; Decision making; Deep learning; deep learning (DL); Drone aircraft; Identification and classification; Image classification; individual tree species (ITS) classification; Machine learning; machine learning (ML); Neural networks; Plant canopies; Plant species; pseudo tree crown (PTC); Spatial discrimination; Spatial resolution; Species classification; Support vector machines; Trees; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Urban planning; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16111849

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.