
Worked on the dataforgoodfr/13_potentiel_solaire repository, building features to enhance geospatial analysis and data validation for solar potential assessments. Developed a Jupyter notebook to analyze and visualize classified buildings in Saint-Denis, integrating the BDTOPO dataset and implementing logic to tag educational facilities within protected zones. Used Python, GeoPandas, and SQL to streamline data retrieval and improve tagging reliability, supporting risk-aware planning and compliance checks. Later, created a Surface Area Accuracy Validation Notebook that compared MNS-derived usable surface area with project approximations, incorporating statistical analysis and visualization to establish a reproducible workflow for validating and tuning solar modeling parameters.
Implemented a Surface Area Accuracy Validation Notebook to validate usable surface area calculations by comparing MNS data with the project approximation, including data visualization and statistical analysis to quantify accuracy and guide parameter tuning.
Implemented a Surface Area Accuracy Validation Notebook to validate usable surface area calculations by comparing MNS data with the project approximation, including data visualization and statistical analysis to quantify accuracy and guide parameter tuning.
February 2025: Delivered Saint‑Denis building analysis and protected-zone tagging in dataforgoodfr/13_potentiel_solaire. Consolidated two commits into a cohesive feature: (1) a new Jupyter notebook for analyzing classified buildings and visualizing educational facility zones using the BDTOPO dataset; (2) data retrieval and processing for protected buildings, including a tag to detect if a school lies within a protected zone, thereby enhancing classification by protection status and school designation. Minor data-pipeline cleanups improved data flow and tagging reliability. No major bugs reported.
February 2025: Delivered Saint‑Denis building analysis and protected-zone tagging in dataforgoodfr/13_potentiel_solaire. Consolidated two commits into a cohesive feature: (1) a new Jupyter notebook for analyzing classified buildings and visualizing educational facility zones using the BDTOPO dataset; (2) data retrieval and processing for protected buildings, including a tag to detect if a school lies within a protected zone, thereby enhancing classification by protection status and school designation. Minor data-pipeline cleanups improved data flow and tagging reliability. No major bugs reported.

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