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
Erin Zimmerman developed a Python-based land cover classification workflow for the Mississippi Delta within the earthlab-education/Earth-Analytics-AY24 repository. She designed an end-to-end pipeline that automated data acquisition, preprocessing, cloud masking, and merging of multispectral Sentinel and Landsat imagery. Using k-means clustering, Erin grouped pixels by spectral signatures to enable automated land cover mapping, and incorporated data visualization techniques to compare cluster results with RGB composites, providing interpretation guidance for land use analysis. Her work demonstrated depth in geospatial analysis, machine learning, and remote sensing, establishing a reusable foundation for future delta-region studies and supporting informed land-use planning decisions.
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 people
Work 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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