Landslide Extraction Using Mask R-CNN with Background-Enhancement Method
Round 1
Reviewer 1 Report
Generally, this is a good work, well conceptualised to study landslide in Yunnan province in China. I have however several observations. Addressing them will certainly enhance international significance and novelty of the work. I outlined my observations below
[1] Abstract: line 25: instead of values, it should be attributes, line 26: change happened to ‘occurred’, line 31: should be underscoring instead of validating
[2] Introduction: This section requires further improvement especially referring landslide studies from other areas need to review and mention. Some studies are suggested which could be useful https://doi.org/10.3390/ijgi10050315; https://doi.org/10.1109/LGRS.2021.3127073; https://doi.org/10.1038/s41598-021-03585-1
Line 54: are you referring to ‘manual’ method here? I don’t know what is hand-operated? Line 93: delete ‘the extraction’ here. Fig. 1b what is epicentre? Is this related to earthquake? Clarify
[3] What optical images were used? Give their detailed attributes
[4] Some figures have captions on top, they must be removed
[5] I don’t find any logic to put fig 13 in page 11, should go to method section
[6] As you are proposing a background enhancement method, I believe doing a sensitivity test will be highly useful to say how this method works against population machine or deep learning algorithms. I am sure this work https://doi.org/10.3390/rs12203347 would give you some insights and pathways
[7] Finally, I am a bit hesitant to see the use of Aug 2014 landslide event for this study? Couldn’t you use recent events as this landslide is too old and there are so many changes occurred in the area by now. Hence the results would not be standing for Yunnan study area
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Dear Authors
The presented work seems to be interesting but needs further corrections and considerations to become an acceptable paper on this journal.
1- Abstract does not present important points. It must concisely mention the problems of previous works and novelties in this paper.
2- Similarly, the introduction has very poor structure and lack of literature review. Usually in the chapter of introduction the background and needs of study of change detection and land slide extraction methods have to be highlighted and prepare readers to go further. Then your second chapter should be literature review where you present an overview on the previous works and the main problem statements of work and how it can be improved or overcome on it.
3- Please provide more information about the selected location and data repository and how they have been collected.
4- Results and discussion are not properly organized and it has to show the significant achievements of the proposed method and discuss each table and figure properly and in detail.
5- As you have lots of abbreviations, It is good that you have provided a table of abbreviations according to MDPI style.
6-It could be helpful if you do a comparison between your proposed method and some of the available or common other methods to show the efficiency of it.
7- It would be great if you provide a general framework or flowchart that how others can implement or use your proposed method for their assessment purposes. (somehow you present it in Fig 13 but give more detail)
8- In general, your conclusion is good and you can discuss a bit again about the achievements and novelty of your proposed method.
9- In total, the main problem of you paper is the lack of literature review and you can present some new developed methods for change detection, application of feed forward training and ML techniques in Landslide detection, vulnerability and damage assessment of buildings, and infrastructures to attract the attention of readers and show a wide view of your works. Below are some of the recent works, where I found them new and useful to add and make your paper much more interesting:
-Evaluation of Change Detection Techniques using Very High Resolution Optical Satellite Imagery
-Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets
-Loess Landslide Detection Using Object Detection Algorithms in Northwest China
-Post-earthquake road damage assessment using region-based algorithms from high-resolution satellite images
-A review on application of soft computing techniques for the rapid visual safety evaluation and damage classification of existing buildings
-Landslide detection using deep learning and object-based image analysis
Author Response
Please see the attachment.
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Thanks
Reviewer 2 Report
Many thanks for the significant changes. However, some of the references we have recommended and you did not use are good to be added as they might attract the interest of the readers to the similar field and other functions of ML/AI techniques and indeed can cause increasing readers of your works.
