ESA-APEx/apex_algorithms
Dec 2025 – Jan 2026
2 Months active
Languages Used
JSONN/APython
Technical Skills
API developmentAPI integrationJSON manipulationN/APython programmingalgorithm design
SHARMAP
PROFILE
Sharmap
Pratichhya Sharma enhanced the ESA-APEx/apex_algorithms repository by developing advanced phenology and NDVI time series analytics, integrating Whittaker smoothing and multi-output Gaussian Process Regression for fusing Sentinel-1 and Sentinel-2 data. She designed robust process graphs and improved data output management, ensuring TIFF outputs and updated documentation for reproducibility. Using Python and JSON, she implemented peak and valley detection in time series and maintained catalog tooling, including UI thumbnail updates and dependency management. Her work also addressed reliability by fixing JSON configuration issues and updating benchmarking references, resulting in more trustworthy land monitoring analytics and streamlined development workflows.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
17Total
Bugs
2
Commits
17
Features
4
Lines of code
2,224
Activity Months2
Your Network
30 people
Work History
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.
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
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.
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.
Activity
Loading activity data...
Quality Metrics
Correctness97.8%
Maintainability93.0%
Architecture97.8%
Performance93.0%
AI Usage27.0%
Skills & Technologies
Programming Languages
JSONN/APython
Technical Skills
API developmentAPI integrationJSON manipulationJSON schema validationN/APython programmingalgorithm designalgorithm developmentalgorithm documentationalgorithm validationdata analysisdata formattingdata managementdata modelingdata processing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline