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The AI in Urban Planning Course gives urban planning, infrastructure, government, and sustainability professionals a comprehensive, structured understanding of how artificial intelligence is transforming city development — covering smart city applications, sustainable infrastructure, predictive modelling, digital twins, public services governance, and the implementation strategies needed to deploy AI responsibly across urban environments.
Cities are under unprecedented pressure from population growth, climate change, mobility demands, and resource constraints and AI is providing urban planners and infrastructure developers with tools to address those pressures at a scale and speed that conventional planning approaches cannot match. From AI-driven land use decisions and smart grid optimisation to real-time traffic management, climate resilience modelling, and digital twin city simulations, the applications are transformative.
This course addresses every dimension of AI in urban planning — from IoT, big data, and machine learning fundamentals, through GIS-based spatial analysis, predictive urban growth modelling, AI in public safety and emergency response, to blockchain and AI for urban data security, and a final AI implementation roadmap for sustainable urban development.
The AI in Urban Planning and Infrastructure Development Course is built for urban professionals who want the knowledge, tools, and strategic capability to design smarter, more sustainable, and more resilient cities using AI.
The AI in Urban Planning and Infrastructure Development Course is designed to develop comprehensive AI application capability across urban planning and infrastructure development — from smart city fundamentals and sustainable infrastructure through predictive modelling, public services governance, and implementation strategy.
By the end of this course, participants will be able to:
The AI in Urban Planning and Infrastructure Development Course is designed for urban planning, infrastructure, government, and sustainability professionals who are responsible for or contributing to city development, smart infrastructure, and the integration of AI into urban governance and planning frameworks.
This course is suitable for:
The AI in Urban Planning and Infrastructure Development Course is delivered through a structured, application-focused learning approach that moves from AI and smart city fundamentals through sustainable infrastructure, data-driven planning, public services governance, and implementation strategy. Each day addresses a distinct urban AI application domain building a complete, integrated understanding of how AI is reshaping cities across planning, infrastructure, governance, and sustainability dimensions.
Case studies of AI-powered urban transformation, spatial analysis discussions, digital twin applications, and a final implementation roadmap session are integrated throughout — ensuring delegates connect AI frameworks to the real planning and governance challenges of urban development.
Delivery methods include:
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Day 2 covers AI in transportation planning and mobility management examining how AI optimises public transport networks, manages traffic flow, supports autonomous and connected vehicle integration, and improves mobility system resilience. Delegates develop a practical understanding of how AI-driven transportation planning is being applied in cities globally and what the implementation considerations are for deploying these tools within existing urban mobility frameworks.
Day 3 covers data-driven urban planning in depth including how GIS and remote sensing data are integrated with AI for spatial analysis, how predictive analytics models urban growth patterns and development demand, and how AI-enhanced environmental impact assessments improve the quality and speed of planning evaluations. Delegates develop a practical understanding of how these spatial and predictive tools are transforming evidence-based urban planning practice.
Ethical considerations and AI governance are addressed within Day 4 — examining the specific ethical challenges of AI in urban planning including surveillance and privacy, algorithmic bias in public service delivery, transparency in AI-assisted planning decisions, and the accountability structures needed when AI influences decisions that affect entire communities. Delegates develop the governance awareness to design and evaluate AI applications in urban contexts that are equitable, transparent, and accountable to the citizens they serve.
Climate adaptation and disaster resilience are addressed within Day 2 covering how AI models are used to predict climate risk, simulate extreme weather scenarios, optimise infrastructure placement for resilience, and improve emergency response coordination in disaster situations. Delegates develop the knowledge to evaluate AI resilience tools against specific climate and disaster risk profiles and to integrate them into city-wide adaptation and emergency planning frameworks.
Digital twins and AI-based city simulations are addressed within Day 4 examining how digital twin platforms create virtual replicas of urban environments that planners and engineers can use to model, test, and optimise planning decisions before implementation. Delegates develop an understanding of how digital twins are being applied to infrastructure planning, traffic management, energy optimisation, and emergency response simulation — and what the data, governance, and investment requirements of implementing city-scale digital twins involve.
Public safety, security, and emergency response applications are covered within Day 4 — examining how AI is applied to crime prediction and prevention, surveillance system management, emergency dispatch optimisation, and AI-assisted healthcare resource allocation in urban settings. Delegates develop a balanced understanding of both the effectiveness and the ethical dimensions of AI in public safety an increasingly important area of urban governance where the stakes for both security and civil liberties are high.
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