IBM/terratorch
Oct 2024 – Oct 2024
1 Month active
Languages Used
Python
Technical Skills
PyTorchdata augmentationdata processingdata visualizationgeospatial analysismachine learning
daniela-szw
PROFILE
Daniela-szw
Worked on the IBM/terratorch repository to enhance data handling for geospatial machine learning workflows. Developed two features that unified dataset normalization and datamodule implementations across multiple datasets, improving consistency and reducing maintenance overhead. Leveraged Python and PyTorch to standardize metadata processing and introduced tensor-based date handling, enabling more flexible and robust deep learning pipelines. Improved code organization and removed hardcoded values, which streamlined onboarding of new datasets and accelerated experimentation. Addressed critical bugs in data visualization and data split logic, resulting in greater cross-dataset compatibility. The work demonstrated strong skills in data processing, data augmentation, and geospatial analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,287
Activity Months1
Your Network
1038 peopleSame Organization
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Work History
October 2024
2 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for IBM/terratorch focusing on delivering robust data handling improvements and critical bug fixes that enable more reliable model training and easier onboarding of new datasets. Key outcomes include two feature enhancements that standardize data normalization and datamodules across multiple datasets, metadata handling standardization with tensor-based date processing for DL workflows, and targeted bug fixes that improve robustness of the data pipeline. These efforts reduce maintenance, accelerate experimentation, and improve cross-dataset interoperability.
2 Commits • 2 Features
Oct 1, 2024October 2024 monthly summary for IBM/terratorch focusing on delivering robust data handling improvements and critical bug fixes that enable more reliable model training and easier onboarding of new datasets. Key outcomes include two feature enhancements that standardize data normalization and datamodules across multiple datasets, metadata handling standardization with tensor-based date processing for DL workflows, and targeted bug fixes that improve robustness of the data pipeline. These efforts reduce maintenance, accelerate experimentation, and improve cross-dataset interoperability.
October 2024
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PyTorchdata augmentationdata processingdata visualizationgeospatial analysismachine learning
Repositories Contributed To
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