[REVIEW]: servir-aces: A Python Package for Training Machine Learning Models for Remote Sensing Applications

[editorialbot]

Submitting author: @biplovbhandari (Biplov Bhandari)
Repository: https://github.com/SERVIR/servir-aces
Branch with paper.md (empty if default branch):
Version: v0.0.16
Editor: @kanishkan91
Reviewers: @nagellette, @jtagusari
Archive: Pending

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Reviewers and authors:

Please avoid lengthy details of difficulties in the review thread. Instead, please create a new issue in the target repository and link to those issues (especially acceptance-blockers) by leaving comments in the review thread below. (For completists: if the target issue tracker is also on GitHub, linking the review thread in the issue or vice versa will create corresponding breadcrumb trails in the link target.)

Reviewer instructions & questions

@nagellette & @jtagusari, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review.
First of all you need to run this command in a separate comment to create the checklist:

@editorialbot generate my checklist

The reviewer guidelines are available here: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html. Any questions/concerns please let @kanishkan91 know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Checklists

📝 Checklist for @jtagusari

📝 Checklist for @nagellette

[editorialbot]

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.90  T=0.10 s (591.8 files/s, 220603.7 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Jupyter Notebook                 6              0          12497           3121
Python                          20            904           1100           2572
Markdown                        20            140              0            355
YAML                             7             19              9            246
TeX                              2             19              0            179
TOML                             1             14              1             62
HTML                             1              2              0              9
-------------------------------------------------------------------------------
SUM:                            57           1098          13607           6544
-------------------------------------------------------------------------------

Commit count by author:

    81	Biplov Bhandari
    13	Tim Mayer
     1	Bibplov Bhandari

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- None

MISSING DOIs

- 10.1016/j.rse.2017.06.031 may be a valid DOI for title: Google Earth Engine: Planetary-scale geospatial an...
- No DOI given, and none found for title: Deep learning approach for Sentinel-1 surface wate...
- 10.3389/fenvs.2023.1137835 may be a valid DOI for title: Employing the agricultural classification and esti...
- No DOI given, and none found for title: Comparing Deep Learning Models for Rice Mapping in...
- 10.3390/rs15051203 may be a valid DOI for title: Delineation of Wetland Areas in South Norway from ...
- 10.1016/j.ophoto.2021.100003 may be a valid DOI for title: Mapping sugarcane in Thailand using transfer learn...
- 10.3390/rs13163166 may be a valid DOI for title: Automatic detection of impervious surfaces from re...

INVALID DOIs

- None

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 739

✅ The paper includes a Statement of need section

[editorialbot]

License info:

🟡 License found: GNU General Public License v3.0 (Check here for OSI approval)

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[kanishkan91]

@biplovbhandari , @jtagusari, @nagellette , This is the review thread for the paper. All of our communications will happen here from now on.

Please read the "Reviewer instructions & questions" in the first comment above.

For @nagellette and @jtagusari - Both reviewers have checklists at the top of this thread (in that first comment) with the JOSS requirements. As you go over the submission, please check any items that you feel have been satisfied. There are also links to the JOSS reviewer guidelines.

As you are probably already aware, The JOSS review is different from most other journals. Our goal is to work with the authors to help them meet our criteria instead of merely passing judgment on the submission. As such, the reviewers are encouraged to submit issues and pull requests on the software repository. When doing so, please mention #6729 so that a link is created to this thread (and I can keep an eye on what is happening). Please also feel free to comment and ask questions on this thread. In my experience, it is better to post comments/questions/suggestions as you come across them instead of waiting until you've reviewed the entire package.

We aim for the review process to be completed within about 4-6 weeks but please make a start well ahead of this as JOSS reviews are by their nature iterative and any early feedback you may be able to provide to the author will be very helpful in meeting this schedule.

Thanks in advance and let me know if you have any questions!!

[kanishkan91]

@editorialbot remind @nagellette in 2 weeks

[editorialbot]

Reminder set for @nagellette in 2 weeks

[kanishkan91]

@editorialbot remind @jtagusari in 2 weeks

[editorialbot]

Reminder set for @jtagusari in 2 weeks

[jtagusari]

Review checklist for @jtagusari

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/SERVIR/servir-aces?
• License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@biplovbhandari) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[nagellette]

Review checklist for @nagellette

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the https://github.com/SERVIR/servir-aces?
• License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@biplovbhandari) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
• Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
• Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
• Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
• Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[nagellette]

@biplovbhandari although there are multiple applications with GEE and Tensorflow in the context of agricultural applications that are mentioned in statement of need section I couldn't find anything actually referring to similar software and comparing yours against to them. One example I can think of is torchgeo which is providing access to different sources (incl. Sentinel and Landsat which I think are also available via GEE) while providing processing and training capabilities in Pytorch. Another example would be raster-vision. I am not aware anything in Tensorflow sourcing from GEE though I suggest looking in to similar software, referring to them while emphasizing servir-aces's contribution.

• torchgeo: https://dl.acm.org/doi/10.1145/3557915.3560953
• raster-vision: https://github.com/azavea/raster-vision

[biplovbhandari]

@jtagusari @nagellette Thanks for your comments and review. I am currently on work travel, hence the delay. I will get back to you soon. Thank you for your patience :)

[editorialbot]

👋 @nagellette, please update us on how your review is going (this is an automated reminder).

[editorialbot]

👋 @jtagusari, please update us on how your review is going (this is an automated reminder).

[kanishkan91]

@biplovbhandari It seems both reviewers have posted some issues and comments regarding the paper and the code. Could you take a pass at the same over the next couple of weeks. Thanks!

After that, I can ask the reviewers to take another pass. @jtagusari and @nagellette - Let me know if this sounds right to you wanted to add something to the above.

[kanishkan91]

@editorialbot remind @biplovbhandari in 2 weeks

[editorialbot]

Reminder set for @biplovbhandari in 2 weeks

[kanishkan91]

While the author responds, @jtagusari could you provide a quick update on your review. I saw that you had checked off a few items on your checklist. Is it that you will check off the remaining once the author makes the adjustments you suggested? Do let me know if you need anything at all from me or the author. Thank you again !

[jtagusari]

@biplovbhandari
Some comments on the paper (which you have revised). It would be good if you could be a little more specific and clear about the differences from existing methodologies:

l. 14-19
The current challenge is that there are gaps in the use of abundant satellite image data in GEE for ML/DL models, and the developed package will fill the gap. It would be better to narrow and specify the scope of the package. In addition, I think the "gap" should be clearly described since GEE provides information about using ML/DL in its product guide ( https://developers.google.com/earth-engine/guides/machine-learning ) and it does not look difficult at first glance.

l. 27-28
You mentioned that existing applications required specialized knowledge and libraries, what did you change that allowed you to apply common libraries?

[editorialbot]

👋 @biplovbhandari, please update us on how things are progressing here (this is an automated reminder).

[biplovbhandari]

@jtagusari I attempt to address the point in the new version. Please check! There is also an updated README for running the package. Thanks!