Urban AI

Our research explores how AI can be used to analyze and predict urban dynamics, enhance urban resilience, and support the design of more sustainable cities.

About

Urban planning decisions are increasingly shaped by data, models, and digital platforms. Our Urban AI research examines how these technologies can be designed, evaluated, and governed so that they support planning practice rather than obscure it.

We develop machine-learning models, decision-support systems, and urban digital twin methods for questions such as urban renewal, socio-economic impact assessment, parcel aggregation, and multi-domain urban simulation. These projects combine spatial data, planning knowledge, and computational modeling to help planners compare alternatives before interventions are implemented.

At the same time, we study the limitations of urban AI: data fragmentation, model bias, poor transferability between cities, and the difficulty of translating technical metrics into planning evidence. Our work therefore emphasizes interpretability, benchmarking, and validation in real planning contexts.

The aim is to advance urban AI as a transparent research and planning infrastructure that can support sustainable, resilient, and socially aware urban development.