Cityweft vs Shapezo: A Practical Pipeline from Map Selection to 3D City Context

Description and use

A staged workflow that separates AI concept generation (Shapezo) from integrated city modeling (Cityweft) reduces surprises and ensures data traceability for accurate analysis.

Problem

Architects and planners need a clear separation between AI-generated concept models and integrated, data‑driven city models to avoid misinterpreting polished concepts as verified project information.

Outcome

The two‑stage pipeline reduces surprises by separating concept generation from city‑model assembly, ensuring that polished AI models are not mistaken for verified data and that integrated models provide accurate, traceable context for analysis.

Workflow

• Define a bounded experiment with a clear question and map boundary.
• Generate a rough AI model in Shapezo and review massing, access, and context.
• Select the best concept and import it into Cityweft.
• Import AutoCAD geometry, GIS layers, BIM content, terrain, roads, and infrastructure into Cityweft.
• Organize all data into a city‑scale view, ensuring spatial references are shared.
• Convert rough AI shapes into meaningful objects, focusing on geometry that affects decisions.
• Validate coordinate systems, scale, terrain quality, and object completeness.
• Export the model to formats like CityGML, OBJ, STL, PLY, or FBX for downstream analysis.
• Use the model as input for daylight, viewshed, traffic, energy, and environmental studies.
• Iterate as needed, replacing AI assumptions with source‑backed geometry.

Evidence

Shapezo lets me select an area on a map and generate a rough model with AI.

Source

Cityweft is where I can combine AutoCAD content, GIS data, BIM models, terrain, roads, and infrastructure into a larger 3D city environment.

Source