google/earthengine-catalog
Jul 2025 – Jun 2026
2 Months active
Languages Used
JavaScriptJsonnetPythonJSONJSONNET
Technical Skills
Data CatalogingGeospatial DataMachine Learning DatasetsRemote SensingEarth Enginedata management
Michelangelo Conserva
PROFILE
Michelangelo Conserva
Contributed to the google/earthengine-catalog by developing and releasing the Farmscapes 2020 dataset, which provides high-resolution probability maps and vectorised polygon geometries for hedgerows, woodlands, and stone walls across England’s agricultural landscapes. Leveraging Python and JavaScript, the work included creating detailed metadata, visualization parameters, and example scripts to support landscape restoration, biodiversity monitoring, and ecological connectivity analyses. Enhanced data accessibility by packaging resources with clear documentation and JSON schemas, enabling reproducible research. Addressed data quality by correcting typographical errors and improving dataset descriptions, ensuring clarity for users and supporting robust geospatial analysis and policy reporting within the Earth Engine ecosystem.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
536
Activity Months2
Your Network
5187 peopleSame Organization
@google.com5154
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Shared Repositories
33
Work History
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 performance summary for google/earthengine-catalog: Delivered a vectorised Farmscapes 2020 dataset with polygon geometries added to the Nature Trace catalog, along with new JSON dataset files and visualization scripts to enhance user interaction and biodiversity monitoring. Implemented a minor quality improvement by correcting typographical errors in the dataset description. These changes broaden landscape feature coverage (hedgerows, woodlands, stone walls) in England, enabling more accurate landscape restoration planning, biodiversity monitoring, and policy reporting.
2 Commits • 1 Features
Jun 1, 2026June 2026 performance summary for google/earthengine-catalog: Delivered a vectorised Farmscapes 2020 dataset with polygon geometries added to the Nature Trace catalog, along with new JSON dataset files and visualization scripts to enhance user interaction and biodiversity monitoring. Implemented a minor quality improvement by correcting typographical errors in the dataset description. These changes broaden landscape feature coverage (hedgerows, woodlands, stone walls) in England, enabling more accurate landscape restoration planning, biodiversity monitoring, and policy reporting.
June 2026
July 2025
1 Commits • 1 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on the google/earthengine-catalog work performed this month. The primary deliverable is the Farmscapes 2020 dataset release with high-resolution probability maps for hedgerows, woodland, and stone walls across England's agricultural landscapes, including metadata, visualization parameters, and example scripts to support landscape restoration, biodiversity monitoring, and ecological connectivity analyses. This work references a Farmscapes dataset publication in Nature Trace, as documented by the commit cc990e0226bef0d582cf4c153cd9c1ce7cb71a22.
July 2025
1 Commits • 1 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on the google/earthengine-catalog work performed this month. The primary deliverable is the Farmscapes 2020 dataset release with high-resolution probability maps for hedgerows, woodland, and stone walls across England's agricultural landscapes, including metadata, visualization parameters, and example scripts to support landscape restoration, biodiversity monitoring, and ecological connectivity analyses. This work references a Farmscapes dataset publication in Nature Trace, as documented by the commit cc990e0226bef0d582cf4c153cd9c1ce7cb71a22.
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJSONNETJavaScriptJsonnetPython
Technical Skills
Data CatalogingEarth EngineGeospatial DataMachine Learning DatasetsRemote Sensingdata managementdata visualizationdocumentationgeospatial analysis
Repositories Contributed To
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