The Potential of Visual ChatGPT for Remote Sensing

Round 1

Reviewer 1 Report (Previous Reviewer 3)

The authors have addressed most of the comments. 

The quality of English is acceptable.

Author Response

Dear Reviewer,

Thank you for your time and effort in reviewing our manuscript. We believe that your feedback has helped improve the quality of our work. Regarding the English language, we performed another revision of the manuscript with a more careful reading. 

Best Regards, 
Lucas Osco.

Reviewer 2 Report (New Reviewer)

This paper analyses the potential of Visual ChatGPT, to execute different image processing tasks related to the remote sensing research ambit. The authors examine some process like classification, edge detection, line detection and segmentation. They evaluate the performance of algorithms with accuracy metrics based on the extraction information derived from scene classification, edge and line detection and image segmentation. From the conclusions, it is derive that Visual ChatGPT offers promising opportunities to be used in the research field of image processing in remote sensing, although at the moment there are some drawbacks that do not improve what is obtained so far by traditional methodologies.

I found the paper to be interesting. The findings are interesting and the topic is relevant to the journal. The paper is well structured and mostly well written.

The results seem agree with the initial objectives and the conclusions confirm these. Further research is needed with continued evolution and adaptation to the specific needs of remote sensing process image tasks.

Some specific concerns are as follows:

1) What is the computational cost of using this type of strategy?

2) In the article, the authors state that Visual ChatGPT can be adapted to assist non-experts in executing edge detection tasks of different objects present in the image. The model proposed by the authors is difficult to use, even for expert researchers. The authors say it is important to note that the current version of Visual ChatGPT has not been yet specifically trained on remote sensing imagery…….

Author Response

Dear Reviewer,

Thank you for your review and comments. We are pleased to know that you found our paper interesting and relevant to the journal. Your feedback has greatly assisted us in improving our manuscript.

Regarding your specific concerns:

1. We agree that a discussion on the computational cost of our strategy is important. We have added a section to the revised manuscript detailing the computational cost of using Visual ChatGPT. Briefly, the computational cost of Visual ChatGPT is slightly higher at the moment since it requires consumable tokens within the OpenAI API. However, we believe that the model's flexibility and potential for future implementations make it a promising option, worthy of further exploration.

2. You correctly pointed out that the current version of Visual ChatGPT may be challenging for non-experts to use. This reflects the early stage of development of this technology. We have expanded our discussion in the manuscript to highlight this issue and have made it clear that while our research shows the potential of Visual ChatGPT, its practical application will require further work to improve its user-friendliness. We, however, believe that soon, we'll see ongoing efforts in improving the usability of such models, including better user interfaces and guidance for both experts and non-experts.

Your comments helped enhance our manuscript. We hope that we have addressed your concerns satisfactorily.

Best Regards, 
Lucas Osco.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Visual ChatGPT is quite hot in recent days. This manuscript explores the potential of ChatGPT for remote sensing images. As far as I am concerned, the most important contribution of this manuscript is to test the ability of ChatGPT in edge detection, segmentation, classification, etc. for remote sensing images. To be honest, I am not sure if this manuscript strictly belongs to “Technical note” in Remote Sensing Journal, since the whole content is about experimental analysis and results obtained based on the existing platform without any theoretical models, equations, or method/experimental improvement. Also, I think the conclusions/advice about the development and improvement of VLMs in remote sensing are very general, which are the consensus problems in the AI field. In terms of this manuscript, these advices are given in lack of comparative tests. In addition to this, I cannot provide any more suggestions.   

Reviewer 2 Report

Indeed interesting work. The authors convinced me of the feasibility of using ChatGPT for remote sensing. While most of the results are not very impressive, some examples are indeed exciting and promising (e.g., Figure 7 mid-row). The authors have developed and presented a working approach for using ChatGPT in remote sensing.

Be sure you use a scientific style in the text. For example: In line 166 "It also gets" -> do not use "gets" in a scientific text (replace it with something like obtains).

Reviewer 3 Report

The paper evaluates the capabilities of Visual Chat GPT for remote sensing data sets. Following are few observations.

1. In line 139 it is mentioned that a portion of the dataset is used without specifying the details. What are the criteria for the selection of the portion?

2. The Visual Chat GPT is used more like a black box without discussing the capabilities of the transformer model. This work is a report of a set of experimentations done on the pre-established algos /Models with no or very small research contribution.

3. The conclusions drawn are very obvious; however, the suggested solutions/ conclusions do not have any sure solution. The statements like  "Future research could focus on optimizing these models for the domain-specific tasks, investigating novel directions, and addressing limitations and biases." is a very generic statement and is true in all cases for all scenarios.

4.  The research article refers to VLM multiple times without any technical detailing. A small introduction would have helped, as is given for the evaluation parameters (with citation). Also, some inferences based on transformer models or visual language model is desired.