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Article

TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images

1
College of Intelligence and Computing, Tianjin University, Tianjin 300072, China
2
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(3), 524; https://doi.org/10.3390/rs16030524
Submission received: 7 November 2023 / Revised: 16 January 2024 / Accepted: 22 January 2024 / Published: 30 January 2024

Abstract

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation.
Keywords: tree location; shape analysis; procedural generation; object detection tree location; shape analysis; procedural generation; object detection

Share and Cite

MDPI and ACS Style

Gong, H.; Sun, Q.; Fang, C.; Sun, L.; Su, R. TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images. Remote Sens. 2024, 16, 524. https://doi.org/10.3390/rs16030524

AMA Style

Gong H, Sun Q, Fang C, Sun L, Su R. TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images. Remote Sensing. 2024; 16(3):524. https://doi.org/10.3390/rs16030524

Chicago/Turabian Style

Gong, Haoyu, Qian Sun, Chenrong Fang, Le Sun, and Ran Su. 2024. "TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images" Remote Sensing 16, no. 3: 524. https://doi.org/10.3390/rs16030524

APA Style

Gong, H., Sun, Q., Fang, C., Sun, L., & Su, R. (2024). TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images. Remote Sensing, 16(3), 524. https://doi.org/10.3390/rs16030524

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