google/earthengine-catalog
Jul 2025 – Jul 2025
1 Month active
Languages Used
JavaScriptJsonnetPython
Technical Skills
Data CatalogingGeospatial DataMachine Learning DatasetsRemote Sensing
Michelangelo Conserva
PROFILE
Michelangelo Conserva
During July 2025, Mconserva developed and released the Farmscapes 2020 dataset within the google/earthengine-catalog repository, delivering high-resolution probability maps for hedgerows, woodland, and stone walls across England’s agricultural landscapes. Leveraging Python, JavaScript, and Jsonnet, Mconserva packaged the dataset with comprehensive metadata, visualization parameters, and example scripts to facilitate landscape restoration and biodiversity monitoring. The work emphasized data cataloging and geospatial data management, aligning with a peer-reviewed publication to ensure scientific rigor. By providing reproducible resources and clear usage examples, Mconserva enabled researchers and practitioners to efficiently analyze ecological connectivity and support robust environmental planning efforts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
232
Activity Months1
Your Network
4182 people
Work History
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.
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
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaScriptJsonnetPython
Technical Skills
Data CatalogingGeospatial DataMachine Learning DatasetsRemote Sensing
Repositories Contributed To
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