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27 November 2024 Individual tree detection in urban land based on multi-source remote sensing data
Ying Ding, Jueyu Lin, Deshuai Zhang, Ying Sun
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Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 134020L (2024) https://doi.org/10.1117/12.3049003
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China
Abstract
Individual tree detection is crucial for ecological analysis in forestry, which plays a key role in effectively constructing land use planning and protecting biodiversity. In this paper, Hartford County, Connecticut, USA, is selected as the research area. Mask-RCNN is used to extract individual trees in the study area, and then the local maximum method is applied for extracting individual trees in high-density areas. The study shows that multi-source remote sensing data can effectively improve the accuracy of individual tree extraction, and a total of 13,274,355 trees are detected in Hartford County. The aim of this study is to provide a more effective method of tree monitoring to help governments prioritize green space management on a super-urban scale.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ying Ding, Jueyu Lin, Deshuai Zhang, and Ying Sun "Individual tree detection in urban land based on multi-source remote sensing data", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 134020L (27 November 2024); https://doi.org/10.1117/12.3049003
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KEYWORDS
Deep learning
LIDAR
Remote sensing
Image processing
Data modeling
Feature extraction
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