EOPF-Sample-Service/eopf-sample-notebooks
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
DaskData AnalysisGeospatial AnalysisPythonSatellite Data ProcessingXarray
Yuvraj198920
PROFILE
Yuvraj198920
Yuvraj Adagale developed a heatwave detection feature for the EOPF-Sample-Service/eopf-sample-notebooks repository, focusing on automated identification of heatwave events using Sentinel-3 Land Surface Temperature data. He implemented an end-to-end data processing pipeline in Python, leveraging Dask and Xarray for efficient handling of large satellite datasets. The solution filtered data by date range and geographic area, computed daily maximum temperatures, and applied a rolling window analysis to detect sustained high-temperature periods. This work enabled automated heat risk assessment, supporting integration with analytics dashboards and reporting pipelines. The feature addressed a clear need for proactive resource planning and response.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
11,931
Activity Months1
Your Network
13 peopleSame Organization
@eurac.edu1
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for EOPF-Sample-Service/eopf-sample-notebooks: Key feature delivered is heatwave detection using Sentinel-3 Land Surface Temperature (LST) data, enabling automated identification of heatwave events within specified date ranges and geographic areas. No major bugs were reported/fixed this month. Overall impact: accelerates heat risk assessment and supports proactive decision-making for resource planning and response. Technologies and skills demonstrated include satellite data processing, daily maximum LST calculation, rolling window analysis for sustained high-temperature detection, and readiness for integration with analytics dashboards and reporting pipelines.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for EOPF-Sample-Service/eopf-sample-notebooks: Key feature delivered is heatwave detection using Sentinel-3 Land Surface Temperature (LST) data, enabling automated identification of heatwave events within specified date ranges and geographic areas. No major bugs were reported/fixed this month. Overall impact: accelerates heat risk assessment and supports proactive decision-making for resource planning and response. Technologies and skills demonstrated include satellite data processing, daily maximum LST calculation, rolling window analysis for sustained high-temperature detection, and readiness for integration with analytics dashboards and reporting pipelines.
June 2025
Activity
Loading activity data...
Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
DaskData AnalysisGeospatial AnalysisPythonSatellite Data ProcessingXarray
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline