dsi-clinic/CMAP
Nov 2024 – Mar 2025
5 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Code FormattingConfiguration ManagementLintingPythonRefactoringComputer Vision
Anna Elizabeth Woodard
PROFILE
Anna Elizabeth Woodard
Andrew Woodard contributed to the dsi-clinic/CMAP repository by engineering robust data pipelines and optimizing deep learning workflows for geospatial image analysis. Over five months, he enhanced dataset loading, label management, and training efficiency, introducing dynamic class handling and GPU-accelerated data processing using Python and PyTorch. His work included refactoring RiverDataset indexing for higher throughput, implementing configurable data augmentation, and stabilizing multi-dataset training with improved logging and reproducibility. Andrew also addressed critical bugs in file handling and normalization, while maintaining code quality through rigorous linting and refactoring. These efforts resulted in a maintainable, performant backend supporting complex machine learning experiments.
Overall Statistics
Feature vs Bugs
68%Features
Repository Contributions
32Total
Bugs
6
Commits
32
Features
13
Lines of code
2,702
Activity Months5
Your Network
26 people
Work History
March 2025
11 Commits • 3 Features
Mar 1, 2025March 2025 CMAP monthly summary focusing on expanding data sources, stabilizing the training pipeline, and improving observability and performance. The work delivered stronger data support across Kane County and River Dataset, improved per-class IoU diagnostics, faster training cycles, and cleaner, more reproducible scripts.
11 Commits • 3 Features
Mar 1, 2025March 2025 CMAP monthly summary focusing on expanding data sources, stabilizing the training pipeline, and improving observability and performance. The work delivered stronger data support across Kane County and River Dataset, improved per-class IoU diagnostics, faster training cycles, and cleaner, more reproducible scripts.
March 2025
February 2025
10 Commits • 3 Features
Feb 1, 2025February 2025 CMAP: Delivered robust multi-dataset training improvements and stability fixes that enable faster, more configurable experiments with River and Kane County data. Highlights include consolidated data loading and training pipeline enhancements for performance and reduced verbosity; standardized label management across River and KC datasets to support dynamic class counts and robust cross-dataset label mixing; a KC dataset inclusion toggle for flexible experiments; a critical River image saving bug fix for labels containing special characters; and overall pipeline stabilization with improved logging and reduced reliance on global state.
February 2025
10 Commits • 3 Features
Feb 1, 2025February 2025 CMAP: Delivered robust multi-dataset training improvements and stability fixes that enable faster, more configurable experiments with River and Kane County data. Highlights include consolidated data loading and training pipeline enhancements for performance and reduced verbosity; standardized label management across River and KC datasets to support dynamic class counts and robust cross-dataset label mixing; a KC dataset inclusion toggle for flexible experiments; a critical River image saving bug fix for labels containing special characters; and overall pipeline stabilization with improved logging and reduced reliance on global state.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: RiverDataset performance optimizations in CMAP, focusing on training efficiency and observability. Implemented a refactor of RiverDataset indexing to support storing multiple geometries per chip, enabling higher-throughput training. Removed DEM usage to reduce data access overhead, and streamlined the training pipeline by disabling non-critical initial test evaluations. Added dataset-size debug prints to improve monitoring and troubleshooting. Overall, no major bugs fixed this month; the emphasis was on performance, stability, and observability.
1 Commits • 1 Features
Jan 1, 2025January 2025: RiverDataset performance optimizations in CMAP, focusing on training efficiency and observability. Implemented a refactor of RiverDataset indexing to support storing multiple geometries per chip, enabling higher-throughput training. Removed DEM usage to reduce data access overhead, and streamlined the training pipeline by disabling non-critical initial test evaluations. Added dataset-size debug prints to improve monitoring and troubleshooting. Overall, no major bugs fixed this month; the emphasis was on performance, stability, and observability.
January 2025
December 2024
9 Commits • 5 Features
Dec 1, 20242024-12 CMAP monthly summary for dsi-clinic/CMAP: Delivered a set of high-impact features to improve data handling, visualization, and training performance, alongside robust fixes that strengthen data quality and stability. The work focused on enabling configurable NIR usage, improving normalization pipelines with ImageNet statistics, accelerating data processing via GPU, and optimizing training dynamics. In addition, code quality and linting improvements were completed to maintain long-term maintainability.
December 2024
9 Commits • 5 Features
Dec 1, 20242024-12 CMAP monthly summary for dsi-clinic/CMAP: Delivered a set of high-impact features to improve data handling, visualization, and training performance, alongside robust fixes that strengthen data quality and stability. The work focused on enabling configurable NIR usage, improving normalization pipelines with ImageNet statistics, accelerating data processing via GPU, and optimizing training dynamics. In addition, code quality and linting improvements were completed to maintain long-term maintainability.
November 2024
1 Commits • 1 Features
Nov 1, 20242024-11 CMAP monthly summary for dsi-clinic/CMAP focused on elevating code quality and maintainability through standardized linting and refactoring. Delivered a major quality improvement feature: Ruff linting compliance upgrade to v0.7.2 with codebase refactors to meet lint rules, including docstring updates, migration to pathlib for file paths, and readability enhancements. No explicit user-facing features or bug fixes documented this month; the primary work reduces technical debt and stabilizes CI quality gates.
1 Commits • 1 Features
Nov 1, 20242024-11 CMAP monthly summary for dsi-clinic/CMAP focused on elevating code quality and maintainability through standardized linting and refactoring. Delivered a major quality improvement feature: Ruff linting compliance upgrade to v0.7.2 with codebase refactors to meet lint rules, including docstring updates, migration to pathlib for file paths, and readability enhancements. No explicit user-facing features or bug fixes documented this month; the primary work reduces technical debt and stabilizes CI quality gates.
November 2024
Activity
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Quality Metrics
Correctness86.6%
Maintainability88.0%
Architecture81.2%
Performance78.8%
AI Usage20.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Backend DevelopmentBug FixBug FixingCode CleanupCode FormattingCode LintingCode MaintainabilityCode RefactoringComputer VisionConfiguration ManagementData AugmentationData EngineeringData LoadingData Pipeline ManagementData Preprocessing
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