IBM/terratorch
Oct 2024 – Sep 2025
6 Months active
Languages Used
PythonMarkdownYAML
Technical Skills
PyTorchdata augmentationdata processingmachine learningPythonPython programming
Pedro Henrique Conrado
PROFILE
Pedro Henrique Conrado
Pedro Henrique Conrado developed and enhanced data pipelines, model training workflows, and dataset management features for the IBM/terratorch repository over six months. He implemented metadata propagation from datasets to models, unified prediction workflows, and introduced new data modules such as OpenEarthMap, improving contextual awareness and reproducibility in machine learning experiments. Using Python, PyTorch, and TorchGeo, Pedro expanded geospatial preprocessing, added robust testing and documentation, and delivered configurable fine-tuning setups for models like Prithvi. His work addressed data reliability, API stability, and flexible data ingestion, demonstrating depth in data engineering and machine learning while ensuring maintainable, scalable, and testable codebases.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
21Total
Bugs
1
Commits
21
Features
10
Lines of code
9,600
Activity Months6
Your Network
66 people
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Monthly performance summary for 2025-09. Focused on delivering a configurable fine-tuning setup for the Prithvi model on the Chesapeake dataset within IBM/terratorch, enabling controlled experiments, reproducibility, and faster time-to-value. No major bugs reported this month; stability maintained across the repository.
1 Commits • 1 Features
Sep 1, 2025Monthly performance summary for 2025-09. Focused on delivering a configurable fine-tuning setup for the Prithvi model on the Chesapeake dataset within IBM/terratorch, enabling controlled experiments, reproducibility, and faster time-to-value. No major bugs reported this month; stability maintained across the repository.
September 2025
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 - IBM/terratorch: Delivered flexible metadata filename support and robust multi-format DateTime parsing for multi-temporal crop classification. No major bugs reported in this period. The changes improve data ingestion reliability and classification accuracy across diverse metadata conventions, reducing manual preprocessing and enabling smoother onboarding of multi-temporal datasets. Technologies demonstrated: Python-based metadata handling, flexible parsing logic, and robust date-time utilities. Commit reference: 971dc64c52c11e740c282bc269c632e920d4e77f.
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 - IBM/terratorch: Delivered flexible metadata filename support and robust multi-format DateTime parsing for multi-temporal crop classification. No major bugs reported in this period. The changes improve data ingestion reliability and classification accuracy across diverse metadata conventions, reducing manual preprocessing and enabling smoother onboarding of multi-temporal datasets. Technologies demonstrated: Python-based metadata handling, flexible parsing logic, and robust date-time utilities. Commit reference: 971dc64c52c11e740c282bc269c632e920d4e77f.
February 2025
6 Commits • 3 Features
Feb 1, 2025February 2025 summary for IBM/terratorch focused on reliability, dataset management, and developer experience. Delivered a targeted bug fix, enhancements to multi-temporal crop classification, expanded testing coverage for data modules, and comprehensive documentation improvements. The work reduces runtime errors, improves data handling across training/validation workflows, increases test coverage, and provides clearer usage guidance for datasets and data modules.
6 Commits • 3 Features
Feb 1, 2025February 2025 summary for IBM/terratorch focused on reliability, dataset management, and developer experience. Delivered a targeted bug fix, enhancements to multi-temporal crop classification, expanded testing coverage for data modules, and comprehensive documentation improvements. The work reduces runtime errors, improves data handling across training/validation workflows, increases test coverage, and provides clearer usage guidance for datasets and data modules.
February 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for IBM/terratorch emphasizing feature delivery and workflow unification. Focused on OpenEarthMap data module, enhanced dataset handling for prediction visualization, and a unified predict workflow across TerraTorch data modules. Implemented predict_transform parameter and per-module predict_dataset creation. Commit activity includes changes such as 'mudancas temporarias' and 'adds predict to datamodules', illustrating concrete feature delivery and groundwork for stable data pipelines.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for IBM/terratorch emphasizing feature delivery and workflow unification. Focused on OpenEarthMap data module, enhanced dataset handling for prediction visualization, and a unified predict workflow across TerraTorch data modules. Implemented predict_transform parameter and per-module predict_dataset creation. Commit activity includes changes such as 'mudancas temporarias' and 'adds predict to datamodules', illustrating concrete feature delivery and groundwork for stable data pipelines.
November 2024
10 Commits • 3 Features
Nov 1, 2024November 2024 (2024-11) — IBM/terratorch delivered substantial improvements to dataset reliability, API hygiene, and geospatial preprocessing, alongside expanding training data coverage. Notable outcomes include comprehensive dataset tests, API stability enhancements by hiding non-working datasets, and a new data_root attribute in FireScarsDataModule. Band handling improvements ensured correct bands propagate to training/validation/testing data loaders, while combined fixes corrected typos and data loader behavior. Geospatial tooling upgrades (Geopandas 14.4) and ToTensorV2 integration, together with the addition of burn-related datasets (burn intensity, carbon flux, landslide detection, forest classification), broadened model training data. These changes contribute to higher data quality, more robust training pipelines, and a clearer, more scalable public API.
10 Commits • 3 Features
Nov 1, 2024November 2024 (2024-11) — IBM/terratorch delivered substantial improvements to dataset reliability, API hygiene, and geospatial preprocessing, alongside expanding training data coverage. Notable outcomes include comprehensive dataset tests, API stability enhancements by hiding non-working datasets, and a new data_root attribute in FireScarsDataModule. Band handling improvements ensured correct bands propagate to training/validation/testing data loaders, while combined fixes corrected typos and data loader behavior. Geospatial tooling upgrades (Geopandas 14.4) and ToTensorV2 integration, together with the addition of burn-related datasets (burn intensity, carbon flux, landslide detection, forest classification), broadened model training data. These changes contribute to higher data quality, more robust training pipelines, and a clearer, more scalable public API.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 20242024-10 monthly summary for IBM/terratorch. Key feature delivered: Dataset Metadata Propagation to Models. This feature enables passing dataset-level metadata to models, allowing models to leverage additional contextual information during training and inference. It required updates to multiple data modules to support new parameters for metadata usage. Commit referenced: 427b394c437034e34f49e41c87c325fb054e1bd0. Major bugs fixed: None reported this month. Overall impact and accomplishments: The metadata propagation capability enhances contextual awareness in model training and inference, improving experiment reproducibility, traceability, and data-model cohesion. It establishes the foundation for metadata-driven workflows and richer analytics across datasets and models, aligning with data governance and model operationalization goals. Technologies/skills demonstrated: Python-based data pipeline enhancements, modular data module design, dataset-to-model metadata propagation, version control discipline, and cross-module integration within the IBM/terratorch repository.
October 2024
1 Commits • 1 Features
Oct 1, 20242024-10 monthly summary for IBM/terratorch. Key feature delivered: Dataset Metadata Propagation to Models. This feature enables passing dataset-level metadata to models, allowing models to leverage additional contextual information during training and inference. It required updates to multiple data modules to support new parameters for metadata usage. Commit referenced: 427b394c437034e34f49e41c87c325fb054e1bd0. Major bugs fixed: None reported this month. Overall impact and accomplishments: The metadata propagation capability enhances contextual awareness in model training and inference, improving experiment reproducibility, traceability, and data-model cohesion. It establishes the foundation for metadata-driven workflows and richer analytics across datasets and models, aligning with data governance and model operationalization goals. Technologies/skills demonstrated: Python-based data pipeline enhancements, modular data module design, dataset-to-model metadata propagation, version control discipline, and cross-module integration within the IBM/terratorch repository.
Activity
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Quality Metrics
Correctness91.4%
Maintainability87.6%
Architecture90.4%
Performance86.6%
AI Usage27.6%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
PyTorchPythonPython programmingTerraTorchTorchGeocomputer visiondata analysisdata augmentationdata engineeringdata handlingdata module testingdata preprocessingdata processingdata sciencedata visualization
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