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Master in AI for Architecture and the Built Environment

IAAC’s Master in AI for Architecture & the Built Environment is a unique program oriented towards leading the change in decarbonising human activities and crafting a more sustainable, resilient future urbanisation for our planet. Through an innovative curriculum deeply rooted in AI applications, the program pioneers novel AI-driven solutions that not only respond to the pressing challenges of our time but also set a new standard for environmentally and socially conscious co-design and planning. The Master in AI for Architecture & the Built Environment is training the professionals that city administrations, governments, industries, and communities need, to transform the built environment in the era of digital technologies.


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CINEFLOW : An AI-Powered Cinematic Walkthrough for Architectural Design

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MaAI-01 2024-2025

Automating Architectural Visualization: A Journey Through Technical Pivots and AI Evolution Introduction In the rapidly evolving landscape of architectural visualization, the gap between conceptual design and compelling visual presentation remains a significant challenge. While architects and designers excel at creating innovative spaces, transforming these 3D models into cinematic walkthroughs typically requires specialized skills, expensive software, … Read more

Adapt_ai: Your copilot for sustainable design

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MaAI-01 2024-2025

Global Problem Buildings account for 40% of global carbon emissions And the majority of these emissions come from operational carbon; a significant component of which is heating, cooling, and lighting. These carbon and financial costs can be reduced by passive design strategies that reduce the need for mechanical heating and cooling, and electrical lighting. Passive … Read more

GG WORLDS Blender Plug-in Prototype

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MaAI-01 2024-2025

GG WORLDS: A Blender plugin that enables users to instantly generate and customize 3D environments from text prompts, using generative AI to save time, cost, and boost creative freedom. Physical Prototype: Our prototype demonstrates an interactive workflow where users arrange physical maps and landmarks, then use a tablet AR app to instantly visualize and modify … Read more

Adapt AI: Unity App

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MaAI 2024-2025

Abstract: For the class of Interactive Interfaces with professor Daniil Koshelyuk we got introduced to the Unity game engine, a powerful software to create all kinds of 3D applications and environments. For our group, we decided to create an app that would be the continuation of our Research Studio project: Adapt AI. A project where … Read more

GG Worlds [v.2]

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MaAI-01 2024-2025

Introduction: From Words to Worlds Game design is one of the most creative fields in digital media today but for most developers, turning ideas into immersive worlds remains slow, expensive, and overwhelming. Especially for independent creators, level design, the art of shaping the physical and emotional journey of a player, is often the most demanding … Read more

Interactive Block-Based Structural Generation and Analysis Tool

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MaAI-01 2024-2025

In contemporary architectural practice, the gap between conceptual design and structural analysis often leads to delays, miscommunication, and inefficient iteration cycles. This project proposes an integrated workflow that bridges this divide by creating an interactive interface where architects can receive real-time structural feedback while scanning their preliminary physical model. By combining Unity-based visualization, QR code-enabled … Read more

Grounded – Structural generation tool for architects

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MaAI 2024-2025

This project proposes a structural prediction tool tailored for the early stages of architectural design. Its goal is to provide architects with a structural assistant that offers meaningful insights, enabling them to make more informed decisions and, in turn, support their creative process from the outset. DEMO As part of the course, a website was … Read more

Urban Vegetation Analysis Using Computer Vision and Deep Learning

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MaAI 2024-2025

(PaintDumpster/environmental_data_workshop at ndvi) – Link to GitHub This project aims to detect and classify different types of greenery in satellite imagery by utilizing color and edge detection techniques. The study area is located in Barcelona, Spain (41.396536, 2.194554). The stable version employs color and edge detection for image segmentation, while an alternative method integrates LiDAR … Read more