earthlab-education/Earth-Analytics-AY24
Mar 2025 – Mar 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
Cloud ComputingData AnalysisData VisualizationGeospatial AnalysisMachine LearningPython
Erin Zimmerman
PROFILE
Erin Zimmerman
Developed a Python-based land cover classification workflow for the Mississippi Delta within the earthlab-education/Earth-Analytics-AY24 repository, focusing on automated mapping from multispectral Sentinel and Landsat imagery. The solution encompassed data acquisition, preprocessing, cloud masking, and merging, followed by k-means clustering to segment pixels by spectral signatures. Visualization components compared clustering results to RGB composites, providing interpretation guidance for land cover types. Leveraging skills in geospatial analysis, machine learning, and data visualization, the workflow established a reusable foundation for delta-region studies. No bugs were reported or fixed during this period, reflecting a focused delivery of a single, end-to-end feature.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
1,704
Activity Months1
Your Network
7 peopleWork History
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a Python-based Mississippi Delta land cover classification workflow within earthlab-education/Earth-Analytics-AY24, enabling automated land-cover mapping from multispectral imagery (Sentinel/Landsat) using k-means clustering. Implemented end-to-end pipeline including data acquisition/downloading, preprocessing, cloud masking, data merging, clustering by spectral signatures, and visualization with an RGB comparison and interpretation guidance. Established a reusable workflow foundation to accelerate delta-region analyses and inform land-use planning decisions.
2 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a Python-based Mississippi Delta land cover classification workflow within earthlab-education/Earth-Analytics-AY24, enabling automated land-cover mapping from multispectral imagery (Sentinel/Landsat) using k-means clustering. Implemented end-to-end pipeline including data acquisition/downloading, preprocessing, cloud masking, data merging, clustering by spectral signatures, and visualization with an RGB comparison and interpretation guidance. Established a reusable workflow foundation to accelerate delta-region analyses and inform land-use planning decisions.
March 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Cloud ComputingData AnalysisData VisualizationGeospatial AnalysisMachine LearningPythonRemote Sensing
Repositories Contributed To
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