elena-andreini/TriesteItalyChapter_PlasticDebrisDetection
Mar 2025 – May 2025
3 Months active
Languages Used
PythonJupyter Notebook
Technical Skills
Data AnalysisData PreprocessingGeopandasGoogle ColabMachine LearningMatplotlib
shaimathamer
PROFILE
Shaimathamer
Shaima Thamer developed an end-to-end marine debris detection pipeline in the elena-andreini/TriesteItalyChapter_PlasticDebrisDetection repository over three months, focusing on plastic debris identification using satellite imagery. She built Google Colab notebooks that automate environment setup, data loading from Google Drive, and preprocessing of MARIDA and MADOS datasets, including resizing and stacking multi-band imagery. Leveraging Python, PyTorch, and Albumentations, Shaima implemented a baseline Attention U-Net model for semantic segmentation, with reproducible workflows supporting rapid prototyping and clear documentation. Her work established a robust foundation for iterative experimentation, enabling streamlined data access, visualization, and model training without major bug fixes required.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
18,414
Activity Months3
Your Network
8 peopleWork History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 summary: Delivered a baseline Google Colab notebook for plastic debris detection, establishing an end-to-end workflow from library setup and Google Drive data loading to visualization of satellite imagery and masks (MADOS/MARIDA) and data preprocessing (resizing, stacking bands) in preparation for U‑Net training. No major bugs fixed this month. Impact: rapid prototyping for debris detection models, enhanced reproducibility, and a solid foundation for iterative experimentation and future deployment. Technologies/skills demonstrated: Python, Google Colab, Google Drive integration, multi-band satellite imagery processing, data visualization, and semantic-segmentation data prep. Top achievements include baseline notebook creation, initial data ingestion and visualization pipeline, and preprocessing steps for U‑Net readiness; commits reflect Colab-based notebook initialization.
2 Commits • 1 Features
May 1, 2025May 2025 summary: Delivered a baseline Google Colab notebook for plastic debris detection, establishing an end-to-end workflow from library setup and Google Drive data loading to visualization of satellite imagery and masks (MADOS/MARIDA) and data preprocessing (resizing, stacking bands) in preparation for U‑Net training. No major bugs fixed this month. Impact: rapid prototyping for debris detection models, enhanced reproducibility, and a solid foundation for iterative experimentation and future deployment. Technologies/skills demonstrated: Python, Google Colab, Google Drive integration, multi-band satellite imagery processing, data visualization, and semantic-segmentation data prep. Top achievements include baseline notebook creation, initial data ingestion and visualization pipeline, and preprocessing steps for U‑Net readiness; commits reflect Colab-based notebook initialization.
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Delivered an end-to-end Marine Debris Detection pipeline in elena-andreini/TriesteItalyChapter_PlasticDebrisDetection, consisting of a Colab notebook for dataset processing (MADOS/MARIDA), a robust preprocessing pipeline to download, organize, resize and stack imagery and labels, and data loaders ready for model training; implemented a baseline Attention U-Net model with integration to MARIDA to enhance debris detection. No major bugs recorded this month; efforts focused on feature delivery and repository maturation. The work improves reproducibility, accelerates prototyping, and strengthens the pipeline from data access through training readiness, driving measurable business value through faster evaluation cycles and clearer documentation.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Delivered an end-to-end Marine Debris Detection pipeline in elena-andreini/TriesteItalyChapter_PlasticDebrisDetection, consisting of a Colab notebook for dataset processing (MADOS/MARIDA), a robust preprocessing pipeline to download, organize, resize and stack imagery and labels, and data loaders ready for model training; implemented a baseline Attention U-Net model with integration to MARIDA to enhance debris detection. No major bugs recorded this month; efforts focused on feature delivery and repository maturation. The work improves reproducibility, accelerates prototyping, and strengthens the pipeline from data access through training readiness, driving measurable business value through faster evaluation cycles and clearer documentation.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a foundational Marine Debris Colab notebook to preprocess and explore MARIDA and MADOS datasets, establishing the environment for subsequent ML tasks in plastic debris detection. The notebook supports library installation, Drive mounting, dataset extraction, initial data exploration, and spectral analysis, enabling faster iteration and reproducibility.
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a foundational Marine Debris Colab notebook to preprocess and explore MARIDA and MADOS datasets, establishing the environment for subsequent ML tasks in plastic debris detection. The notebook supports library installation, Drive mounting, dataset extraction, initial data exploration, and spectral analysis, enabling faster iteration and reproducibility.
March 2025
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Quality Metrics
Correctness78.4%
Maintainability76.6%
Architecture78.4%
Performance66.6%
AI Usage40.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
AlbumentationsCloud Storage IntegrationComputer VisionData AnalysisData AugmentationData LoadingData OrganizationData PreprocessingData ProcessingData VisualizationDeep LearningEnvironment SetupFile HandlingGeopandasGoogle Colab
Repositories Contributed To
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