ESA-APEx/apex_algorithms
Dec 2025 – Jul 2026
7 Months active
Languages Used
JSONN/APythonYAMLMarkdown
Technical Skills
API developmentAPI integrationJSON manipulationN/APython programmingalgorithm design
SHARMAP
PROFILE
Sharmap
Over seven months, contributed to the ESA-APEx/apex_algorithms repository by developing and refining geospatial analytics pipelines for satellite-derived products. Built end-to-end workflows for phenology, NDVI, LAI, and SAR-based CropSAR1D/2D processing, integrating multi-sensor data fusion and benchmarking automation. Leveraged Python, Jupyter Notebooks, and OpenEO API to enable reproducible, parameterized data production and interactive demonstrations. Enhanced backend integration with Copernicus Data Space and Terrascope, standardized data models using JSON and geometry formats, and improved deployment through CI/CD and configuration management. Addressed data integrity and output quality, ensuring scalable, reliable analytics and streamlined developer experience for land monitoring and remote sensing applications.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
97Total
Bugs
5
Commits
97
Features
24
Lines of code
65,829
Activity Months7
Your Network
41 peopleSame Organization
@vito.be17
Work History
July 2026
17 Commits • 3 Features
Jul 1, 2026July 2026 monthly summary for ESA-APEx/apex_algorithms: Implemented foundational Cropsar1D/2D pipelines and Terrascope geospatial integration, plus notebooks and environment scaffolding to support reproducible experiments and deployment readiness. This month established end-to-end SAR processing scaffolds, improved parameter typing, and geospatial processing support, delivering tangible business value in faster prototyping, consistent configurations, and ready-to-run geospatial analyses.
17 Commits • 3 Features
Jul 1, 2026July 2026 monthly summary for ESA-APEx/apex_algorithms: Implemented foundational Cropsar1D/2D pipelines and Terrascope geospatial integration, plus notebooks and environment scaffolding to support reproducible experiments and deployment readiness. This month established end-to-end SAR processing scaffolds, improved parameter typing, and geospatial processing support, delivering tangible business value in faster prototyping, consistent configurations, and ready-to-run geospatial analyses.
July 2026
June 2026
28 Commits • 7 Features
Jun 1, 2026June 2026 monthly summary for ESA-APEx/apex_algorithms: Delivered a set of end-to-end platform enhancements across vision processing, data modeling, and deployment configurations. Emphasis was placed on enabling Cropsar2D first vision push, refining the process graph, standardizing platform schema and references for interoperability, and stabilizing releases through targeted bug fixes and rollbacks. Maintenance work on dependencies, temporal parameters, and benchmarks sets groundwork for scalable analytics, while deployment/configuration updates improve traceability and integration with downstream systems.
June 2026
28 Commits • 7 Features
Jun 1, 2026June 2026 monthly summary for ESA-APEx/apex_algorithms: Delivered a set of end-to-end platform enhancements across vision processing, data modeling, and deployment configurations. Emphasis was placed on enabling Cropsar2D first vision push, refining the process graph, standardizing platform schema and references for interoperability, and stabilizing releases through targeted bug fixes and rollbacks. Maintenance work on dependencies, temporal parameters, and benchmarks sets groundwork for scalable analytics, while deployment/configuration updates improve traceability and integration with downstream systems.
May 2026
19 Commits • 3 Features
May 1, 2026May 2026 delivered end-to-end, OpenEO-ready data workflows for the ESA-APEx/apex_algorithms repository, with a focus on parameterization, reproducibility, and business value. The month highlighted scalable data production, interactive demonstrations, and benchmarking automation that underpin repeatable decision support for satellite-derived products.
19 Commits • 3 Features
May 1, 2026May 2026 delivered end-to-end, OpenEO-ready data workflows for the ESA-APEx/apex_algorithms repository, with a focus on parameterization, reproducibility, and business value. The month highlighted scalable data production, interactive demonstrations, and benchmarking automation that underpin repeatable decision support for satellite-derived products.
May 2026
April 2026
15 Commits • 7 Features
Apr 1, 2026April 2026 performance snapshot for ESA-APEx/apex_algorithms: Delivered a focused set of backend and data-model improvements that elevate data freshness, reliability, and cross-repo consistency. Implemented Copernicus Data Space backend integration for forest fire mapping with an updated model metadata URL, enhanced the Sentinel-2 Spatial Texture Analysis UDF with maintainable scikit-learn import patterns, and aligned benchmark references to refreshed images and media. Added CropSAR JSON serialization and config handling to enable structured outputs and flexible workflows, and modernized spatial extent representation to a geometry format for consistent handling across the repository. These changes reduce data drift, improve reproducibility, and enable scalable analytics and deployment pipelines.
April 2026
15 Commits • 7 Features
Apr 1, 2026April 2026 performance snapshot for ESA-APEx/apex_algorithms: Delivered a focused set of backend and data-model improvements that elevate data freshness, reliability, and cross-repo consistency. Implemented Copernicus Data Space backend integration for forest fire mapping with an updated model metadata URL, enhanced the Sentinel-2 Spatial Texture Analysis UDF with maintainable scikit-learn import patterns, and aligned benchmark references to refreshed images and media. Added CropSAR JSON serialization and config handling to enable structured outputs and flexible workflows, and modernized spatial extent representation to a geometry format for consistent handling across the repository. These changes reduce data drift, improve reproducibility, and enable scalable analytics and deployment pipelines.
February 2026
1 Commits
Feb 1, 2026February 2026: Delivered a targeted fix in ESA-APEx/apex_algorithms to update phenology JSON references to new benchmark data locations, ensuring accurate data retrieval for phenological analysis. This change stabilizes data paths, reduces risk of assertion failures, and improves the reliability of downstream analytics in phenology workflows.
1 Commits
Feb 1, 2026February 2026: Delivered a targeted fix in ESA-APEx/apex_algorithms to update phenology JSON references to new benchmark data locations, ensuring accurate data retrieval for phenological analysis. This change stabilizes data paths, reduces risk of assertion failures, and improves the reliability of downstream analytics in phenology workflows.
February 2026
January 2026
2 Commits
Jan 1, 2026Concise monthly summary for 2026-01 focused on key contributions in the ESA-APEx/apex_algorithms repository, highlighting reliability improvements and benchmarking hygiene that enable more trustworthy outcomes and faster iteration.
January 2026
2 Commits
Jan 1, 2026Concise monthly summary for 2026-01 focused on key contributions in the ESA-APEx/apex_algorithms repository, highlighting reliability improvements and benchmarking hygiene that enable more trustworthy outcomes and faster iteration.
December 2025
15 Commits • 4 Features
Dec 1, 2025Performance summary for 2025-12 (ESA-APEx/apex_algorithms) Key features delivered: - Phenology and NDVI time series analytics enhancements: added NDVI-based phenology metrics via Phenolopy, Whittaker smoothing for NDVI series, refined process graphs, a new result-saving mechanism, and updated reference data for phenology benchmarks. - Peaks and valleys detection in time series: introduced peakvalley detection with benchmark data support and accompanying documentation. - Multi-output Gaussian Process Regression (MOGPR) data fusion service: replaced legacy service with a new implementation that integrates Sentinel-1 and Sentinel-2 data using multi-output Gaussian process regression; updated parameters and a new data fusion process graph. - Catalog maintenance and tooling improvements: cleanup of example outputs, main-branch references, UI thumbnail updates, dependency updates (scikit-image), and UDF improvements for mapping and texture analysis. Major bugs fixed and quality improvements: - Corrected output formats (ensured TIFF outputs where appropriate instead of JSON-like representations) and updated corresponding records descriptions. - Fixed typos and documentation gaps; updated process graphs to reflect current workflows. - UI and catalog references stabilized to support reproducible runs and onboarding. Overall impact and accomplishments: - Enhanced decision quality for land monitoring through richer phenology/NDVI analytics and robust, multi-sensor data fusion. - Improved reliability, reproducibility, and developer experience through catalog/tooling improvements and clearer documentation. Technologies/skills demonstrated: - Time-series analytics (phenology metrics, NDVI smoothing), benchmarking, and output management. - Multi-output Gaussian Process Regression (MOGPR) and data fusion with Sentinel-1/2. - Process graph design, UDF enhancements, and Python ecosystem usage (scikit-image). - Data quality, versioning, and documentation practices.
15 Commits • 4 Features
Dec 1, 2025Performance summary for 2025-12 (ESA-APEx/apex_algorithms) Key features delivered: - Phenology and NDVI time series analytics enhancements: added NDVI-based phenology metrics via Phenolopy, Whittaker smoothing for NDVI series, refined process graphs, a new result-saving mechanism, and updated reference data for phenology benchmarks. - Peaks and valleys detection in time series: introduced peakvalley detection with benchmark data support and accompanying documentation. - Multi-output Gaussian Process Regression (MOGPR) data fusion service: replaced legacy service with a new implementation that integrates Sentinel-1 and Sentinel-2 data using multi-output Gaussian process regression; updated parameters and a new data fusion process graph. - Catalog maintenance and tooling improvements: cleanup of example outputs, main-branch references, UI thumbnail updates, dependency updates (scikit-image), and UDF improvements for mapping and texture analysis. Major bugs fixed and quality improvements: - Corrected output formats (ensured TIFF outputs where appropriate instead of JSON-like representations) and updated corresponding records descriptions. - Fixed typos and documentation gaps; updated process graphs to reflect current workflows. - UI and catalog references stabilized to support reproducible runs and onboarding. Overall impact and accomplishments: - Enhanced decision quality for land monitoring through richer phenology/NDVI analytics and robust, multi-sensor data fusion. - Improved reliability, reproducibility, and developer experience through catalog/tooling improvements and clearer documentation. Technologies/skills demonstrated: - Time-series analytics (phenology metrics, NDVI smoothing), benchmarking, and output management. - Multi-output Gaussian Process Regression (MOGPR) and data fusion with Sentinel-1/2. - Process graph design, UDF enhancements, and Python ecosystem usage (scikit-image). - Data quality, versioning, and documentation practices.
December 2025
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Quality Metrics
Correctness88.4%
Maintainability85.8%
Architecture83.6%
Performance82.4%
AI Usage38.4%
Skills & Technologies
Programming Languages
JSONMarkdownN/APythonYAML
Technical Skills
API developmentAPI integrationAlgorithm DevelopmentBackend IntegrationCI/CDCloud StorageContinuous IntegrationData EngineeringData ScienceData SerializationData VisualizationDependency ManagementDevOpsFile ManagementGeospatial Analysis
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