dsi-clinic/CMAP
Dec 2024 – Mar 2025
3 Months active
Languages Used
PythonJupyter Notebook
Technical Skills
Configuration ManagementData EngineeringDataset IntegrationGeospatial AnalysisGeospatial Data AnalysisPyTorch
Anna Poon
PROFILE
Anna Poon
Anna Poon contributed to the dsi-clinic/CMAP repository by developing and integrating features focused on geospatial data engineering and training pipeline optimization. She unified Kane County and River datasets to enable river-centric model training, refining label and color mappings and introducing configurable options for flexible experimentation. Anna improved data loading and preprocessing by optimizing DEM data sources and tuning training hyperparameters, which enhanced both data quality and training efficiency. Throughout her work, she emphasized code quality and maintainability, performing extensive linting and documentation updates. Her contributions leveraged Python, Jupyter Notebook, and PyTorch, establishing a robust foundation for future geospatial analyses.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
4,778
Activity Months3
Your Network
27 peopleSame Organization
@fe01.ds.uchicago.edu4
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 focused on delivering a training data configuration and DEM data source optimization for CMAP, improving data quality and training efficiency. The work unified dataset loading and DEM data sources, tuned key training hyperparameters (batch size, learning rate, number of workers), adjusted RiverDataset sampling limits, and prioritized differential DEM data over baseline DEM data. Changes were implemented with code cleanups and DEM setting adjustments (commits: e2e25d9c14dd32a11d1b5f10b9c0ae26fb0242a3; 26534d6cb6269191dd7827f52bd4f4bffea7fd6e).
2 Commits • 1 Features
Mar 1, 2025March 2025 focused on delivering a training data configuration and DEM data source optimization for CMAP, improving data quality and training efficiency. The work unified dataset loading and DEM data sources, tuned key training hyperparameters (batch size, learning rate, number of workers), adjusted RiverDataset sampling limits, and prioritized differential DEM data over baseline DEM data. Changes were implemented with code cleanups and DEM setting adjustments (commits: e2e25d9c14dd32a11d1b5f10b9c0ae26fb0242a3; 26534d6cb6269191dd7827f52bd4f4bffea7fd6e).
March 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for dsi-clinic/CMAP. Focused on code quality and maintainability improvements. Delivered a feature that enhances Python script and Jupyter Notebook quality by addressing linting errors and formatting. These changes reduce technical debt, improve readability, and align with style guidelines, setting the stage for more robust development and easier onboarding.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for dsi-clinic/CMAP. Focused on code quality and maintainability improvements. Delivered a feature that enhances Python script and Jupyter Notebook quality by addressing linting errors and formatting. These changes reduce technical debt, improve readability, and align with style guidelines, setting the stage for more robust development and easier onboarding.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 CMAP: Delivered Kane County (KC) data integration with River Dataset (RD) for training, enabling river-focused model training with a merged KC+RD dataset. Updated RiverDataset to support KC data, refined river feature label and color mappings, and added a configurable option to enable river data training. Performed linting cleanups and small class adjustments to improve maintainability. Updated docs to reflect the new training workflow and options. This work improves data quality for river-centric predictions, enables targeted experimentation, and reduces ongoing maintenance burden.
3 Commits • 1 Features
Dec 1, 2024December 2024 CMAP: Delivered Kane County (KC) data integration with River Dataset (RD) for training, enabling river-focused model training with a merged KC+RD dataset. Updated RiverDataset to support KC data, refined river feature label and color mappings, and added a configurable option to enable river data training. Performed linting cleanups and small class adjustments to improve maintainability. Updated docs to reflect the new training workflow and options. This work improves data quality for river-centric predictions, enables targeted experimentation, and reduces ongoing maintenance burden.
December 2024
Activity
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Quality Metrics
Correctness76.6%
Maintainability83.4%
Architecture73.4%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Code QualityConfiguration ManagementData EngineeringData LoadingData PreprocessingDataset IntegrationGeospatial AnalysisGeospatial Data AnalysisLintingPyTorchPython Development
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