Miao, S.; Zhang, K.F.; Zeng, H.; Liu, J. Improving Artificial-Intelligence-Based Individual Tree Species Classification Using Pseudo Tree Crown Derived from Unmanned Aerial Vehicle Imagery. Remote Sens.2024, 16, 1849.
Miao, S.; Zhang, K.F.; Zeng, H.; Liu, J. Improving Artificial-Intelligence-Based Individual Tree Species Classification Using Pseudo Tree Crown Derived from Unmanned Aerial Vehicle Imagery. Remote Sens. 2024, 16, 1849.
Miao, S.; Zhang, K.F.; Zeng, H.; Liu, J. Improving Artificial-Intelligence-Based Individual Tree Species Classification Using Pseudo Tree Crown Derived from Unmanned Aerial Vehicle Imagery. Remote Sens.2024, 16, 1849.
Miao, S.; Zhang, K.F.; Zeng, H.; Liu, J. Improving Artificial-Intelligence-Based Individual Tree Species Classification Using Pseudo Tree Crown Derived from Unmanned Aerial Vehicle Imagery. Remote Sens. 2024, 16, 1849.
Abstract
Urban tree classification is pivotal in urban planning and management, facilitating informed decision-making processes. In this study, we introduce a novel data presentation method termed Pseudo Tree Crown, designed to enhance the accuracy and efficiency of urban tree classification. Leveraging the latest advancements in artificial intelligence (AI), we employ a state-of-the-art classification scheme, PyTorch, to maximize the accuracy of tree classification. Our results demonstrate a robust classification accuracy of over 95% from high spatial resolution imagery from Unmanned Aerial Vehicle (UAV), underscoring our proposed approach’s effectiveness. Moreover, the adaptability of our method renders it applicable to various study areas, highlighting its versatility and potential for widespread implementation in urban planning and management initiatives.
Keywords
Pseudo Tree Crown (PTC); PyTorch; Artificial Intelligence (AI); Unmanned Aerial Vehicle (UAV); individual tree species (ITS) classification
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.