Machine Learning in Urban Design
Pattern Recognition / Morphological Analysis
Keshava Narayan, Ashish Tiwari
Our scope involved developing an AI-driven framework to decode the underlying rules of successful urban spaces. We focused on mapping historical housing patterns and identifying how these can be re-interpreted for modern, high-density residential developments.
The main challenge was the qualitative nature of urban “context.” We developed a methodology to quantify environmental, social, and aesthetic factors into a digital format that can be processed by neural networks for predictive design.
We created a custom “Context Decoder” tool that allows architects to input topographical and cultural data to generate design guidelines. This ensures that new buildings are inherently compatible with their heritage while utilizing modern construction techniques.
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