Next Article in Journal
Towards a New Paradigm for Digital Health Training and Education in Australia: Exploring the Implication of the Fifth Industrial Revolution
Previous Article in Journal
Non-Surgical Lower-Limb Rehabilitation Enhances Quadriceps Strength in Inpatients with Hip Fracture: A Study on Force Capacity and Fatigue
 
 
Article
Peer-Review Record

Towards Intricate Stand Structure: A Novel Individual Tree Segmentation Method for ALS Point Cloud Based on Extreme Offset Deep Learning

Appl. Sci. 2023, 13(11), 6853; https://doi.org/10.3390/app13116853
by Yizhuo Zhang, Hantao Liu, Xingyu Liu and Huiling Yu *
Reviewer 1:
Appl. Sci. 2023, 13(11), 6853; https://doi.org/10.3390/app13116853
Submission received: 6 May 2023 / Revised: 28 May 2023 / Accepted: 2 June 2023 / Published: 5 June 2023

Round 1

Reviewer 1 Report

1-The segmentation is the main part of the paper but the following paper do it better than you for a wide range of trees type:

Chen, X.; Jiang, K.; Zhu, Y.; Wang, X.; Yun, T. Individual Tree Crown Segmentation Directly from UAV-Borne LiDAR Data Using the PointNet of Deep Learning. Forests 2021, 12, 131. https://doi.org/10.3390/f12020131

2- In the lines 314 to 318 the parameters are used in running the proposed method are presented, but there is no reason why these parameters are set to that amount?

3- As the results show the proposed method has about 10% better than other methods, there is no comparison with the time process consuming for reaching this advantage over other methods.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The author introduced a top-down segmentation strategy to propose an adaptive segmentation method based on extreme offset deep learning. Based on my review, I suggest some comments to revise the paper. 

1. I suggest author to discuss the organization of sections at end of the introduction section. 

2. Improve the quality of Figure 1

3. I suggest to include future directions of the work in conclusion section.

 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

thank you very much for your valuable answers to the comments.

 

Back to TopTop