Artificial Intelligence and Machine Learning

dcom.png Project background The entire lifecycle of the built environment is governed by a variety of regulations, requirements and standards. The checking of compliance against these is a complex task, which is currently performed manually, thus becoming resource intensive. Recently, the increased maturity of information...

screenshot_2020-07-01_at_12.23.50.png Project background This network takes an interdisciplinary approach to understanding the state-of-the-art in use of modelling support for infrastructure planning decision making, both in industry and policy practice, and in the research community; needs of the practitioner community...

screenshot_2020-07-01_at_12.06.22.png Project background This research network aims to take a user-centric approach to interacting with information, from search and retrieval to browsing and exploration, and to consider the following three dimensions: (1) the lifecycle of information, from its creation, delivery, capture...

vision.png Project background This research network contributes to developing the research agenda, enhancing the level of performance and digitisation for future smart cities and smart construction at citizen, portfolio, organisation and project levels. The objective of the Vision Network is to delineate the current...

crowdsourcing.png Project background This project aims to understand how check-in data from social media is distributed around Cambridge and what kinds of spatial segmentation can be identified. It validated the social media data on urban segregation using observation and questionnaires. We conducted pilots at Cambridge in...

machine_learning.png Project background This project improved the foundations for applying tried-and-tested machine learning (ML) approaches to the built environment. This mini project reduced the cost of creating and deploying ML systems by creating versatile and extendable Application programming interfaces (APIs), data...