Expose fitted planar roof facets with pitch, azimuth, and RMSE metrics.
npx skills add https://github.com/wwwillott/jobnimbus --skill roof-facets
Please help me install this Agent Skill. Skill: roof-facets Source: https://github.com/wwwillott/jobnimbus/tree/main/skills/research/roof-facets Command: npx skills add https://github.com/wwwillott/jobnimbus --skill roof-facets
This skill exposes the fitted planar roof facets, along with pitch, azimuth, and RMSE quality metrics, enabling precise roof modeling from elevation data.
Run the roof_facets.py script with a target address to output the facet model summary and metrics.
High-intent search queries and answers about installing and using this skill.
You extract roof pitch and azimuth from DSM elevation data by fitting planar roof facets to the elevation model, which outputs the geometric properties and quality metrics for each roof plane.
A roof facet model represents fitted planar roof segments derived from elevation data, and RMSE validates it by measuring the residual error between the fitted planes and the original DSM surface points.
You get a facet grid from a DSM for roofing analytics by running a plane-fitting pipeline on the elevation data, which segments the roof into individual planar facets and returns them as a structured grid.
Yes, you can use this roof facet extraction approach for civil engineering prototypes, as it provides reusable Python client outputs for facet grids and quality metrics suitable for validating segmentation pipelines.
The best way to compare roof facet extraction approaches in architectural research is to evaluate the facet count, RMSE, pitch, and azimuth metrics produced by the planar fitting pipeline across different elevation datasets.
Your roof facet extraction returns a high RMSE when the fitted planar model poorly aligns with the DSM surface, often indicating complex non-planar roof geometry or noisy elevation data in the segmented facets.
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