The studio aims to employ the students with fundamental conceptual and practical skills for developing data-driven AI methodologies for urban planning problems.
MaCAD AI in Architecture Studio
According to the OECD’s projection, 70% of the world population will live in cities by 2050 [1]. By then, we will need to build around 13.000 buildings per day [2]. Yet, today we have very little understanding of the impact of these buildings on our natural, social and economic environment. In the wake of the global pandemic that has so significantly changed how we operate in cities and in anticipation of the now inevitable climate emergency, there has never been a better moment to radically redefine how we perceive, conceive and construct our urban environment. Urban transformation is much more than a buzzword today and it requires quite challenging and interdisciplinary approaches.
Still, our urban environments generate enormous amounts of data that remains unused for resilience planning. Our urban environments today are much more than building typologies and massing morphologies and involve complex structures and networks of human and environmental activity. Climatic conditions, energy, several layers of mobility networks, data and communication flows, economic and cultural activity, political and societal forces and even viruses are all governing how our cities look, feel and function.
In the “AI for Urban Resilience” studio we aim to unveil the immense potential of novel AI methods combined with big open data and open source technologies in developing new approaches to drive our urban regeneration processes. The vision of the studio is to develop working methods based on real data to solve real world problems of the case study city. Our aim is to use our collectively invested time to develop implementable and replicable solutions that we can apply today to promote resilience planning in our cities.
We also aim to change the architectural education regime and disrupt both the speculative and unrealistic experimentation as well as stressful and toxic project development, by employing actual project planning, roles, accountability and time management throughout the studio.
The studio aims to employ the students with fundamental conceptual and practical skills for developing data-driven AI methodologies for urban planning problems. Specifically, the learning objectives of the studio are:
There are different types of loneliness and different causes for each type. The social media induced loneliness as described by Sherry Turkle in her book Alone Together: Why We Expect More from Technology and Less from Each Other (2012) is different from the loneliness of feeling alone in a crowd in a very large city … Read more
Rental Market and Vacancy Rates Growth Rate in Real Estate Market Income Status and Population Rates Homeless Rates and City of Melbourne Affordable Housing Strategy Available Affordable Housing Apartment One of the existing affordable housing is the Markham Avenue Apartments which is located in Ashburton, a residential community in the City of Boroondara, just 12 … Read more
Insightful Predictions for Your City’s Future. The purpose of Foresight is to leverage the capabilities of machine learning to anticipate the potential effects of novel urban infrastructure on the city’s fabric and the value of city blocks. Foresight aims to serve as a preliminary design tool, with the ability to swiftly process vast amounts of … Read more
The assignment entailed harnessing the open data portal for the city of Melbourne with the aim to articulate a narrative revolving around data and technology within the cityscape. Our group initiated contact with several individuals possessing firsthand experience of Melbourne, who provided valuable insights steering us away from our preliminary topic of the potential correlation … Read more
Problem statement and project objectives How can we create a routing system that orients onto its users environment, utilizing open source data? How do we collect and process data? Our objective is to utilize the existing data and generate any missing data required for each aspect of our analysis. We combine data from different sources … Read more
