## 6 Conclusions  
In this paper, an ITS method based on extreme offset deep learning is designed for complex stand structures. The key steps are as follows: (1) preprocessing: (2) extreme offset; (3) self-adaptive clustering; (4) space mapping; (5) set aggregation; (6) postprocessing. In order to verify the universality and accuracy of the proposed method, confierous forest plots in the Blue Ridge area of Washington, USA, and mixed forest plots near Bretten, Germany, are selected as the test plots. The point density is low, and the stand structure is relatively simple in the coniferous forest of the Blue Ridge in the USA. The point density is high, and the stand structure is complex in the mixed forest of Bretten in Germany. The test of the algorithm in these two areas can effectively verify the universality and accuracy of the algorithm. The experimental results show that after the introduction of step (2) extreme offset, it can effectively enhance the discrimination among different trees and then improve the ITS accuracy (the average \(p\), \(r\), and \(F\) reach 0.79, 0.72, and 0.75, respectively). After the introduction of step (2) and step (3) adaptive bandwidth strategy, it has better segmentation accuracy and can better adapt to complex scenes and changing environments (the average \(p\), \(r\), \(F\) reach 0.87, 0.85, 0.85, respectively). After the introduction of step (2) and step (5) ITS set aggregation, it can effectively reduce the over-segmentation error and improve the segmentation accuracy of the proposed method (the average \(p\), \(r\), \(F\) reach 0.91, 0.76, 0.83, respectively). After the introduction of steps (2), (3) and (5), the average \(p\), \(r\), and \(F\) of the proposed method in all plots reach 0.91, 0.88, and 0.90, respectively. In the future, we intend to improve the proposed method from the following aspects. (1) Improve the deep network to better adapt to the ITS task, (2) the tree trunk point coordinates may be extracted using the leafless data and fed into the deep network as features for learning, and (3) appropriate data augmentation methods may be used to enhance the learning ability of the network. In summary, the experimental results effectively verify the universality and accuracy of the proposed method and show the superiority and application potential in segmenting complex forest environments by comparing them with other algorithms.  
[PERSON] and [PERSON] designed the method and performed the experiments; [PERSON] analyzed and preprocessed the data; [PERSON] and [PERSON] reviewed and revised the manuscript; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available on [[https://doi.pangaea.de/10.1594/PANGAEA.94](https://doi.pangaea.de/10.1594/PANGAEA.94)]([https://doi.pangaea.de/10.1594/PANGAEA.94](https://doi.pangaea.de/10.1594/PANGAEA.94)) 2856 (accessed on 1 January 2022) and [[http://forsys.cfr.washington.edu/FUSION/fusion_overview.html](http://forsys.cfr.washington.edu/FUSION/fusion_overview.html)]([http://forsys.cfr.washington.edu/FUSION/fusion_overview.html](http://forsys.cfr.washington.edu/FUSION/fusion_overview.html)) (accessed on 11 June 2018).  
We acknowledge the [PERSON] et al., and the pacific northwest research station USDA forest service for providing data for this research.  
The authors declare no conflict of interest.