[Notebook PR] Update classification_of_sfm_derived_point_clouds_using_deep_learning.ipynb

[neha-sharma-geoai]

Summary of Enhancements Made to the Notebook

The notebook has been improved for usability, automation, clarity, and reproducibility.
Below is a consolidated list of all changes implemented:

1. Updated Notebook Language
Improved overall readability and consistency.
Enhanced clarity of explanations and instructions throughout the notebook.

2. Expanded Comments and Markdown
Added detailed comments within code cells.
Added richer markdown cells with clear explanations, diagrams, and context.

3. Added Cells to Download & Extract Raw Dataset
Automated extraction improves setup reliability.

4. Masked Username in Paths for Security
Updated scripts to dynamically construct user paths without displaying usernames.
Increased notebook portability and security.

5. Added Markdown Explaining Directory Structures
Raw dataset structure
Exported PCTD dataset structure
Saved model folder structure
Helps users understand the pipeline and generated artifacts.

6. Added Cells to Build Local Paths (Train/Val/Export)
Automated construction of train, validation, and export paths.
Reduces manual path-handling errors.

7. Added Markdown Explaining PCTD Format
Explained why PCTD is required, its components, and how deep learning tools use it.

8. Automated Exporting of Training Dataset
The notebook now performs PCTD export directly using ArcPy, instead of requiring user input.
Fully automated end-to-end data preparation.

9. Fixed show_batch Error
Issue created, resolved by Vaibhav and Vikash.
Verified and validated within the notebook.

10. Added Screenshots for show_batch and show_results
Plotly graphs do not render on the website, so screenshots were used to ensure visibility.

11. Added Markdown Explaining Training Cell Output
Clarifies the meaning of logs, metrics, and training progress indicators.

12. Added Per-Class Performance Metrics
Provides users with insight into class-wise model performance.

13. Saved Model with Timestamp
Ensures unique versioning.
Prevents overwriting of previous models.

14. Added Cell to Fetch the Published Model
After saving, the notebook retrieves the ArcGIS Online/Enterprise item for reference.

15. Added Cells to Build Paths to Saved Model & Test Data
Automatically constructs paths used in the inferencing step.

16. Added ArcPy Script for Inferencing in Notebook
Now supports automated classification using arcpy.ddd.ClassifyPointCloudUsingTrainedModel within the notebook itself.

17. Added a 3D Web Scene to Visualize the Output
Integrates web-scene viewer to display classified outputs interactively.

18. Improved Introduction and Conclusion
A clearer explanation of the workflow.
Organized start and end sections for readability.

19. Ensured Notebook is “Run-All” Compatible
All dependencies, paths, and variables are automatically set.
Notebook can now run top-to-bottom without manual inputs.

20. Datasets maintained at \ndhfs2\Data\Jupyter_Sample_Notebooks_Data\Classification_of_sfm_derived_point_clouds_using_deep_learning

21. Published datasets available here - https://geosaurus.maps.arcgis.com/home/content.html?sortField=modified&sortOrder=desc&view=table&folder=60e58089d4264e4781a9149c516586c2#my

Please let me know if any further modifications are required.

[review-notebook-app]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

---

Powered by ReviewNB