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.
(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
The project is situated in Athens, Greece, which overheats in the summer. We wanted to test a hypothesis: The overheating is the result of too much hardscaping (asphalt, concrete, brick) and not enough greenery. We wanted to compare the NDVI (a measure of greenery in a city) taken from Satellite images to Land Surface Temperature … Read more
GenAI for Game Environment Creation OverviewUrban environments in games play a crucial role in crafting immersive, dynamic worlds that captivate players and enhance storytelling. These settings not only shape the game’s atmosphere but also introduce strategic challenges and reflect cultural, social, and architectural identities, making virtual spaces feel authentic. However, creating high-quality, realistic game environments … Read more
AI for Robotic Fabrication Real-time control in fabrication spaces is essential for enhancing operational efficiency, minimizing risks, and ensuring worker safety. In dynamic manufacturing environments, accurately locating and tracking elements—such as machinery, tools, materials, and personnel—can drastically reduce accidents, optimize workflows, and streamline resource management. Through this project, we aim to integrate advanced sensing technologies, … Read more
In pick-and-place robotics, a robotic arm must move from a start position to a target location (e.g., to pick or place an object) while safely navigating around obstacles. These obstacles may vary in size, severity, or risk — requiring the robot to adapt its path based on the workspace condition. This project simulates the core … Read more
The work presented in this blog post explores how code and visual tools can be used to create and represent the data central to this research group’s main project in the Research Studio. It focuses on integrating data-driven design methods to support sustainable façade development while aligning with environmental guidelines. By emphasizing the creation and … Read more
The work presented in this blog post is an approach combining Robotics and Microcontrollers as preparation for a main Research Studio project focused on sustainable facades and environmental guidelines. This represents our first steps in exploring these fields and their connections with Artificial Intelligence in Architecture and the Built Environment. In the Robotics domain, the … Read more
This project leverages Graph Retrieval-Augmented Generation (Graph RAG) to provide intelligent facades configuration recommendations aligned with the New European Bauhaus (NEB) principles. This post presents how we apply AI theory approaches for reaching the stablished target. The project correlates quantitative data and qualitative guidelines, by integrating 17% of the assessed metrics in this guidelines and creating … Read more
