Submission 454, Petitpierre/di Lenardo/Rappo

[mtwente]

Pull request

Proposed changes

• add submission 454 (from https://github.com/RPetitpierre/digihistch24)

Co-authored-by: Rémi Petitpierre [email protected]
Co-authored-by: Isabella di Lenardo [email protected]
Co-authored-by: Lucas Rappo [email protected]

Types of changes

• New feature (non-breaking change which adds functionality).
• Enhancement (non-breaking change which enhances functionality)
• Bug Fix (non-breaking change which fixes an issue).
• Breaking change (fix or feature that would cause existing functionality to change).

Checklist

• I have read the README document.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.
• I have mentioned all co-authors in the PR description as Co-authored-by: Name <[email protected]>.

Summary by CodeRabbit

• New Features

  • Introduced a configuration file for a manuscript project using the Quarto framework, structuring content for academic publication.
  • Launched a comprehensive study on the automation of vectorization of historical cadastral plans, detailing methodology and findings.
  • Added a bibliography featuring key references in cadastral computing and historical data analysis.

• Chores

  • Created a requirements.txt file listing essential libraries for project dependencies, improving setup and environment management.

[coderabbitai]

Caution

Review failed

The pull request is closed.

Walkthrough

This update introduces a manuscript project using Quarto, centering on the automation of vectorization for historical cadastral plans in Switzerland. It includes an index file that details the research methodology and findings, a bibliography to support the study, and a requirements file for necessary dependencies. These additions enhance the project’s structure and accessibility, facilitating efficient research and analysis of land ownership dynamics over time.

Changes

Files	Change Summary
submissions/454/_quarto.yml	New configuration file for manuscript project, defining structure and output format (HTML).
submissions/454/index.qmd	Introduced comprehensive study on the automation of vectorization of historical cadastral plans using neural networks.
submissions/454/references.bib	New bibliography file with diverse entries supporting cadastral computing and historical data analysis.
submissions/454/requirements.txt	New file specifying project dependencies: matplotlib, numpy, and pandas with versions.

Poem

🐰 In fields of data, I hop and play,
With maps and charts, I find my way.
Vectorize the past, oh what a sight,
Cadastral wonders, shining bright!
With each new file, my heart takes flight,
A tale of land, in digital light! 🌟

---

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[coderabbitai]

Actionable comments posted: 0

Outside diff range, codebase verification and nitpick comments (1)

submissions/454/index.qmd (1)

105-125: Consider removing unused imports.

The import statement for tqdm is used, but json is not utilized in the provided code. Consider removing it if it's not needed.

-import shapefile, tqdm, cv2, json
+import shapefile, tqdm, cv2

Review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between ca413bc and caff7c9.

Files ignored due to path filters (10)

• submissions/454/images/center_2020.svg is excluded by !**/*.svg
• submissions/454/images/center_berney_v2.svg is excluded by !**/*.svg
• submissions/454/images/center_melotte_v2.svg is excluded by !**/*.svg
• submissions/454/images/center_renove.svg is excluded by !**/*.svg
• submissions/454/images/owners_chart.svg is excluded by !**/*.svg
• submissions/454/images/persistence_1727_1831_light.svg is excluded by !**/*.svg
• submissions/454/images/persistence_1831_1888_light.svg is excluded by !**/*.svg
• submissions/454/images/persistence_1888_2020_light.svg is excluded by !**/*.svg
• submissions/454/images/prelaz_renove.svg is excluded by !**/*.svg
• submissions/454/images/segmentation.svg is excluded by !**/*.svg

Files selected for processing (4)

• submissions/454/_quarto.yml (1 hunks)
• submissions/454/index.qmd (1 hunks)
• submissions/454/references.bib (1 hunks)
• submissions/454/requirements.txt (1 hunks)

Files skipped from review due to trivial changes (3)

• submissions/454/_quarto.yml
• submissions/454/references.bib
• submissions/454/requirements.txt

Additional comments not posted (17)

submissions/454/index.qmd (17)

1-34: Metadata is well-structured and complete.

The metadata section includes all necessary information such as submission ID, title, authors, keywords, and bibliography. Ensure that the ORCID IDs and email addresses are accurate.

---

36-100: Narrative sections are clear and well-organized.

The narrative provides a comprehensive overview of the research, data collection, and analysis. It is well-supported by references and figures. Ensure that all references are correctly formatted and accessible.

---

129-159: Function createIndex handles invalid geometries well.

The function correctly skips null or empty geometries and attempts to fix invalid ones. It returns an updated GeoDataFrame and index. Ensure that the handling of invalid geometries aligns with the overall data processing strategy.

---

161-189: Function matchParcels efficiently identifies geometry matches.

The function uses bounding box overlap to identify potential matches and calculates overlap ratios. Ensure that the overlap threshold aligns with the research objectives.

---

191-203: Function reverseMatches correctly reverses matching relations.

The function builds a reverse mapping of matches efficiently. Ensure that this reverse mapping is used appropriately in subsequent analysis.

---

205-215: Function create_polygons handles invalid geometries appropriately.

The function attempts to fix invalid geometries and filters out small polygons. Ensure that the area threshold aligns with the research objectives.

---

217-223: Function overlap_percentage handles exceptions effectively.

The function calculates overlap percentages and handles exceptions gracefully. Ensure that the exception handling aligns with the overall error management strategy.

---

225-243: Function calculate_angles accurately computes turning functions.

The function calculates angles and normalizes the turning function. Ensure that the calculations align with the intended geometric analysis.

---

246-278: Function compare_polygons effectively compares polygon shapes.

The function computes the minimum area between turning functions for comparison. Ensure that the comparison method aligns with the research objectives.

---

280-314: Function create_image generates images from polygon data.

The function processes pixels and assigns intensity based on turning scores. Ensure that the image generation aligns with the visualization needs of the project.

---

316-353: Function computeDynamics effectively computes fusion dynamics.

The function processes one-to-one and many-to-one matches to compute dynamics. Ensure that the methodology aligns with the research objectives.

---

355-375: Function convert_multipolygon_to_polygon handles conversion appropriately.

The function fills gaps and shrinks polygons back after conversion. Ensure that the conversion process aligns with the data processing requirements.

---

377-407: Function compute_legend generates a color palette and legend.

The function calculates colors based on weights and generates a legend image. Ensure that the color scheme aligns with the visualization goals.

---

411-418: Data loading and filtering is correctly implemented.

The code loads shapefiles and filters data based on specified classes. Ensure that the file paths and filtering criteria align with the data processing needs.

---

422-430: Geometry matching is efficiently implemented.

The code creates indices and matches geometries between datasets. Ensure that the matching logic aligns with the research objectives.

---

434-443: Persistence detection and data preparation are accurate.

The code computes dynamics and prepares data for visualization. Ensure that the calculations align with the research objectives.

---

447-469: Persistence plot generation is correctly implemented.

The code generates persistence plots and saves images in different modes. Ensure that the visualization aligns with the project's goals.

[maehr]

@mtwente Thanks for adding submission 454. I am not sure whether the inline code is the best solution for our audience and for the later pdf compilation. We should discuss this tomorrow.

[maehr]

@RPetitpierre @lucasrappo Thank you very much for your submission. We decided to comment out your code section for the moment. Please provide us with the missing shape files in the data folder and link the code to the relevant sections in the text if possible (as for example in https://digihistch24.github.io/book-of-abstracts/submissions/462/). Please also add the programming language and code-fold instructions to your code blocks as shown below

#| code-fold: true
import shapefile, tqdm, cv2, json
import numpy as np
import pandas as pd
...

We really appreciate that you provide code and we would love to render it on our platform as a superb example of portable code.

Best
Moritz

[RPetitpierre]

Dear Moritz @maehr,

Thank you for your message and sorry for the late reply. I was on holidays with no access to my email until Monday. I also found a minor but nasty bug in the code which took me a bit of time to fix.

The dynamic figure computation is not very adapted in this case, since the code takes a bit over an hour to run. Alternatively, I suggest to use a smaller data sample, which already greatly helps to understand the methodology and provides an exemplar result, covering just a neighborhood.

I updated the code according to your suggestions. I also updated the other data file (requirements, data, images) to match the generated sample result.

Don't hesitate to write me if you need anything else from my side, and thank you for all the work on organizing this conference, I am looking forward!

Best,

Remi

[maehr]

Dear Moritz @maehr,

Thank you for your message and sorry for the late reply. I was on holidays with no access to my email until Monday. I also found a minor but nasty bug in the code which took me a bit of time to fix.

The dynamic figure computation is not very adapted in this case, since the code takes a bit over an hour to run. Alternatively, I suggest to use a smaller data sample, which already greatly helps to understand the methodology and provides an exemplar result, covering just a neighborhood.

I updated the code according to your suggestions. I also updated the other data file (requirements, data, images) to match the generated sample result.

Don't hesitate to write me if you need anything else from my side, and thank you for all the work on organizing this conference, I am looking forward!

Best,

Remi

Thank you very much. It doesn't matter if it takes long to run, we could try the whole data set. Quarto allows for freezing to safe compute. @mtwente Can you please take a look at the update?