Thank you for your comments and your help in increasing the quality of this manuscript.

The suggestions are well appreciated. We might indeed need to be more clear on wood observations verses wood dynamics observations. We have also had more comments about the figure and will try to make it clearer. Furthermore we will add a section where we will discuss the limitations of low cost cameras and further elaborate.

Thank you very much for the comments, we appreciate the time taken to revise our manuscript and the suggestions that will contribute to a significant increase in quality of the paper. In the following, we reply to each of your comments. In the coming weeks, we will revise and make all changes accordingly in our revised version.

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First, there is limited to no comparison with other existing models. CNNs are widely used for image recognition (and, indeed, the quthors acknowledged YOLO being the most wide spread algorithm), yet, there is no comparative analysis with other studies or algorithms.

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Thanks for the comment, we understand the concern and we agree that we can elaborate more on the alternative methods from different fields and create a more detailed comparison. The current state of the art is only discussed briefly and the paper will benefit by explaining more clearly what the gaps are that the proposed method fills. Also, different techniques from other fields (for instance lidar tech) can be added to the comparison. We will address in the introduction and in the discussion of the revised manuscript. We will introduce the current state of the art in the field wood detection, and expand the use of CNN in other similar fields, like for example, fish passage or plastic transport. For example, adding more past and recent works, like:

https://www.sciencedirect.com/science/article/pii/S0169555X24001351

https://www.jads.nl/case/river-plastic-monitoring/

https://www.sciencedirect.com/science/article/pii/S0303243422000083

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019EA000960

https://www.sciencedirect.com/science/article/pii/S146419090185005X    

https://academic.oup.com/icesjms/article/80/7/1911/7240285

In addition to the aforementioned models, we will add machine learning baselines to justify the use of the YOLO architecture compared to simpler computer vision algorithms:

• A standard convolutional neural network (also informally referred to as a “vanilla CNN”)

• Region-based convolutional neural networks

• Non-ML computer vision baselines, using, e.g., thresholding and edge detection

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Second, the overall aim and output of this manuscript is really unclear. It is necessary to explicitate this further and emphasise what the study has revealed and what increase in scientific knowledge it has brought. As things stand, it is hard to discern what is the new scientific knowledge that this paper has produced.

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Thanks for rising this issue. We understand that the goal of our study was not clear enough, and will edit the text to fix this. The goal of our work was to develop an algorithm that is able to detect and track floating wood pieces in any river and under various conditions. Such an algorithm did not exist at the moment, as the current tools are site specific and require site-specific calibration. The future applications of our algorithm will also be further explained. The immediate use of our CNN is the computation of wood fluxes to better understand wood dynamics in rivers, but there are also many practical applications, such as warning systems or flood risks estimations. We will expand this in the introduction and the discussion of the revised manuscript.

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Third, the paper structure needs substantial changes. The results and discussion sections merged together makes difficult to discern between the actual observations and the authors' analysis. It is essential that the two sections are kept separate. The language used is also not appropriate for a scientific paper: this was mostly informal and colloquial and needs thorough revision.

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Thank you for pointing out this was not clear. We will separate the results and discussion section and work on the structure for a clearer storyline. We will also go through the paper and revise the language carefully to make it less informal.

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Fourth, the method was unclear and lacked explanation (at times it was not even easy to understand what cameras have been used, where and how, whilst a schematic would have helped). Overall, this limits the generalisation of the method proposed.

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We understand this concern and see that there were some confusing parts in the methods section. We added the source references for the CNN methods, but we will expand this to provide additional details about the , the collection of data (e.g. sites, devices, etc,), the machine learning methods, the labeling and the processing. The table of the datasets will be updated and elaborated, and we will add a more clear figure to support the section.

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Fifth: An annotated version is also provided with in-line comments.

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Thanks for the annotations, we will revise the text following each suggestion, and will show our changes tracked in the revised manuscript.