Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys

Tsung-Wei Ke; Stella X. Yu; Mark D. Koneff; David L. Fronczak; Luke J. Fara; Travis J. Harrison; Kyle L. Landolt; Enrika J. Hlavacek; Brian R. Lubinski; Timothy P. White

doi:10.1371/journal.pone.0288121

Peer Review History

Original SubmissionFebruary 16, 2021
23 May 2021 Decision Letter - Yuanquan Wang, Editor PONE-D-21-04667 Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys PLOS ONE Dear Dr. Ke, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. According to the PLOS ONE’s publication criteria , not on the novelty, I would like to recommend "Major Revision" by condersing the following concens, 1) some papers related to this topic would be of great interest for many of its potential readers, including the following examples: (1) Quantification of left ventricle via deep regression learning with contour guidance, IEEE Access 7: 47918-47928 (2019) (2) Automatic Segmentation of the Femur and Tibia Bones from X-ray Images Based on Pure Dilated Residual U-Net, Inverse Problems and Imaging, 10.3934/ipi.2020057 (3)GVFOM: a novel external force for active contour based image segmentation, Information Sciences 506 (2020) 1–18 (4) Automatic segmentation of the cardiac MR images based on nested fully convolutional dense network with dilated convolution, Biomedical Signal Processing and Control, Volume 68, July 2021, https://doi.org/10.1016/j.bspc.2021.102684 2) Annotation for the training dataset should be paraphrased more since it will affect the model and the results. 3) When it comes to the experiments, the cross-validation experiments for the different areas should be employed for validation and the MASK RCNN with and without classification should be taken into account. 4) pls refer to the reviewers' other messages for revision. 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Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). 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You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The major theme of this paper is to evaluate the performance of deep learning models for monitoring of wildlife populations. Two core techniques are involved, including 1) a binary classifier algorithm as a means to filter images based on predicted bird occurrence and 2) a Mask R-CNN algorithm for detecting individual marine bird instances. On the whole, the application project of the paper is interesting, but the novelty in the methodology is rather limited. In a great sense, the paper merely applies existing methods/algorithms to the specific application field. On my side, it should not be submitted as a research paper, and hence I cannot give positive assessment on it. Reviewer #2: The authors proposed a workflow for wildlife population surveys. In this work, they designed algorithms for imagery filtering and bird detection. It is an effective way to apply the classification model before detection. However, there are also some points that should be addressed: 1. Did authors apply multiple annotation for the training dataset? The accurateness of the annotation provides not only the guarantee of the training and testing authenticity, but also the upper boundary of the trained model. 2. It is great to use different survey areas to evaluate the robustness of the model. For better validation, it is encouraged to apply the cross-validation experiments for the different areas, such as training on images from the Lake Michigan and testing on images from the Nantucket Shoals, training on images from both two areas and testing from both two areas, which can fully report the superiority of the proposed method. 3. How did authors choose the probability threshold in the first stage? 4. The authors compared the MASK R-CNN with classification model in the first stage. However, I think it is better to apply the comparisons between MASK R-CNN without classification model and MASK R-CNN with classification model. 5. For the results of detection, did author consider the deviation/error of the first stage, e.g., classification model? ****** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review?** For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. 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Revision 1
4 Aug 2022 Author Response [R1. Lack of novelty] We clarify that this paper does not aim at proposing new classification / detection algorithms, but an automated workflow to support large-scale wildlife surveys. Endowed by the great success of deep learning algorithms, we alleviate the painful need of storing and manually labeling large amounts of in-flight imagery. We combine the state-of-the-art classification and detection methods for filtering images with any bird occurrence and detecting the bird instances correspondingly. In this paper, our main focus is to provide some insights of deep learning algorithms to address issues of data processing in wildlife surveys. [R2. Multiple annotation for the imagery] In this paper, we explore such prototype models for automating data processing and do not include multiple annotations for the imagery. But this is a good suggestion and we are in agreement regarding the utility of this approach. We are developing an operational annotation and training data development workflow in which at least 2 annotators label the same set of imagery, with a process for arbitrating annotation discrepancies through a third species expert. This will allow us to better assess and control annotation data quality. [R2. Cross-validating Mask R-CNN] We agree that this is a good suggestion. We updated the cross-validation experiments in the manuscript. [R2. Probability threshold selection for filtering] In this paper, we simply select the probability threshold based on the performance over 78 additional images from Lake Michigan. We have not yet determined the specific threshold that we will use operationally and realize that the threshold may vary among surveys given specific survey objectives. Operationally, our workflow will incorporate some sampling of the images which do not pass the filter to manually review them and determine evaluate errors of omission. In a related effort we are working to develop statistical population estimation frameworks that incorporate machine-learning derived outputs in a rigorous manner. Errors of commission, passing imagery with no targets to subsequent workflow stages, primarily affect the in-flight performance and computational burden, and are less of our concern. [R2. Compare Mask R-CNN with/without a classifier for filtering] In the first stage of image filtering, we do not consider the fine-grained categories of bird instances, but only focus on detecting any bird occurrence. We, therefore, disagree with the recommendation to compare Mask R-CNN with and without a classifier for image filtering. We do agree that it would be a good idea to compare Mask R-CNN with / without a classifier for the third stage. In our future work, we could conduct comparison between two different settings: 1) detection using the Mask R-CNN without a classifier and categorization using additional classification model, 2) detection and categorization using the Mask R-CNN with a classifier. [R2. Compute the deviation of the first stage] If we are understanding the comment correctly, no we did not evaluate the effects of error in the first stage classifier(filter) downstream in individual detection or classification stages. Our primary concern is how machine-learning outputs and errors at each stage in our workflow propagate as ecologically meaningful uncertainty in wildlife population estimates. While outside the scope of this paper, we are, therefore, focused questions about how machine-learning processes and outputs integrate within standard statistical frameworks for wildlife population size estimation. We envision, and are investigating, series of linked submodels that propagate error at all stages of the workflow (annotation, filter, detection, classification) into ecologically interpretable estimates of uncertainty in population size. Attachments Attachment Submitted filename: Response to Revieweres.pdf https://doi.org/10.1371/journal.pone.0288121.r002
20 Jun 2023 Decision Letter - Judi Hewitt, Editor Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys PONE-D-21-04667R1 Dear Dr. Ke, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Judi Hewitt Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #3: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #3: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #3: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #3: To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, the authors evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. This paper has high application value. But the novelty is limited. ****** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #3: No ******** https://doi.org/10.1371/journal.pone.0288121.r003
Formally Accepted
11 Mar 2024 Acceptance Letter - Judi Hewitt, Editor PONE-D-21-04667R1 PLOS ONE Dear Dr. Ke, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Judi Hewitt Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0288121.r004

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