Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe article demonstrates an approach to drone-based mapping to reduce the human workload required for georeferencing aerial images. I don’t see the novelty in this work. What is the main scientific contribution of this article? Following are my other concerns:
Can the GCP be used to automate the geo-referencing process without the use of the commercial software?
Is there any role of detected SIFT/SURF points in Hough transformation?
Authors have mentioned that all raw images underwent manual annotation by proficient photogrammetry operators. If this task needs to be replicated by other readers, what are the processes that needs to taken care of? Is two floating point enough to secure precise geo-coordinates?
Orthorectification: How GCPs and precise coordinates obtained from RTK-GNSS are implemented to correct the misaligned images. Is any work done in this regard, this could have more contribution to the scientific world.
Pre-processing: What kind of padding parameter was implemented to prevent issues arising from tiles where the GCP center lay at the image edge.
Results: What does Rho parameter within the Hough Lines Transform signify?
What is the rationale behind choosing 3 ResNet architecture and not preferring other deep learning architectures for comparison?
The author mentions that the investigation into automating GCP detection in drone imagery via a purely deep learning approach is a novel contribution in the literature. However, the implementation of deep learning for GCP detection cant be called as a novel contribution. Hence, work needs to be carried out towards a contribution of the article to the scientific community.
Comments on the Quality of English LanguageModerate editing of English language is required.
Author Response
Please refer to uploaded word document.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsAutomating GCP detection in drone imagery: from computer vision to deep learning with remotesensing-2764671-peer-review-v1 was submitted by Gonzalo Muradás Odriozola, Klaas Pauly, Samuel Oswald and Dries Raymaekers.
This manuscript provided well documented background including Ground Control Points, Convolutional Neural Networks and Residual Convolutional Neural Network with data collected from drones in crop agriculture areas. In particular, the research team introduced a procedure of data collection and different analysis of collected data during four different seasons with technical guidelines of manufacturer, a type of model and so on. It induces next international research teams to take benefits building up the following research objectives and outcomes based on previous activities with trials and errors, and approaches. Some comments are as following:
1. It would be better to have brief information of the study area such as its geographical locations and maps, and a type of crop at the beginning of Section 2 Materials and Methods.
2. Describe what the Rho parameter is with some sentences in line 304. It’s helpful for audiences.
3. In lines 480 to 483, suggest a couple of comments for applying to real-world scenarios, if a research group/institute is interested in your approach. Any anticipated problem, different deep learning methods or combination with ground truthing (in-situ data samplings) at the site?
4. In lines 498 to 500, is there any influence of natural light illumination conditions such as clear sky, overcast, or spotty cloud (or cirrocumulus cloud) during drone operations? it could be helpful to describe it in terms of ‘image irregularities’.
5. I suggest the latest publications for your references as it follows:
Becker, D., & Klonowski, J. (2023). Object Recognition of a GCP Design in UAS Imagery Using Deep Learning and Image Processing—Proof of Concept Study. Drones, 7(2), 94.
Buchsteiner, C., Baur, P. A., & Glatzel, S. (2023). Spatial Analysis of Intra-Annual Reed Ecosystem Dynamics at Lake Neusiedl Using RGB Drone Imagery and Deep Learning. Remote Sensing, 15(16), 3961.
Comments on the Quality of English Language
.
Author Response
Please refer to uploaded word document.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsN/A
Comments on the Quality of English LanguageN/A
Author Response
Dear reviewer,
Thank you very much for taking time to read the revised version of our paper. We have applied a couple minor changes to the manuscript to better reflect the State-of-the-Art of the field. The attached document tracks said changes, including their references.
Best regards,
Gonzalo
Author Response File: Author Response.pdf
