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AI is helping to shape our physical world. Advancements in AI, data science, and machine learning are aiding the development of novel opportunities to influence our everyday experiences.
For instance, the application of AI is being used to tackle climate change. AI, machine learning and deep learning are also being used to inform architecture and urban design.
Find out how UCL researchers are using AI to solve natural and built environment issues below.
Built environment
CarbonBuzz
CarbonBuzz aims to raise the understanding of carbon emissions from buildings by providing a platform to benchmark and track project energy use from design to operation.
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Classification of big point cloud data using cloud computing
The 3DIMPact Research Group use machine vision approaches to understand accurate, precise and reliable measurements of manmade and natural objects and structures, applied to building information modelling, smart cities and others. Point cloud data plays a significant role in various geospatial applications. The 3DIMPact Research Group is addressing point data classification in a big data context, using computing framework Apache Space in large-scale point data processing.
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ENFOLDing: Explaining, modelling and forecasting global dynamics
The ENFOLDing research programme aims to develop new approaches to problems presented by global change through the study of four related global systems: trade, migration, security and development aid.
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Knowledge Transfer Partnership: Big data in the office
The Knowledge Transfer Partnership project ‘Big Data in the Office’ aims to create a decision-making tool for clients to better manage space usage in commercial buildings by synthesising and digitising building, organisational and spatial data collected in workplace consultancy projects.
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LUCID: High-resolution GIS maps of the London heat island
By providing high-resolution GIS maps of the London heat island, LUCID aims to contributing to the identification of heatwave vulnerability ‘hotspots’ across the Greater London Area.
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Schools Indoor Pollution and Health: Observatory Network in Europe
The Schools Indoor Pollution and Health: Observatory Network in Europe (SINPHONIE) project contributed to the development of a large-scale European school indoor air quality database across 25 countries, which assesses the effect of indoor environmental quality on the health of primary school children.
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Total performance of low carbon buildings in China and the UK
Research at the Institute for Digital Innovation in the Built Environment, part of the Bartlett School of Construction and Project Management, centres on digital advances, such as building information modelling (BIM), smart cities, big data, AI and the internet of things and how they improve processes and cost-efficiencies to promote sustainable futures. UCL and the Tsinghua University School of Architecture (Beijing) are conducting a study that intends to lead understanding and improvements in the total performance of low carbon buildings helping to develop effective policies and regulations in the transition to low carbon cities.
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Urban Dynamics Lab
The Urban Dynamics Lab at UCL is a five-year EPSRC-funded research project bringing together expertise from across UCL to explore and address questions at the intersection of city and regional development and spatial analytics, data science and computing.
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Communications and connected systems
Adaptive streaming for virtual reality
Researchers in the Department of Electronic and Electrical Engineering (EEE) are collaborating with Adobe Systems to optimise the adaptive streaming platform for Virtual Reality (VR), uses machine learning algorithms to predict user behaviour when navigating content in VR environments.
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TRANSNET
TRANSNET is an EPSRC-funded multidisciplinary research programme aiming to transform optical networks. Led by UCL, in collaboration with Aston and Cambridge Universities, TRANSNET aims to create an adaptive intelligent optical network - able to dynamically provide capacity where and when it is needed - to transform the next-generation digital communications infrastructure.
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Longitudinal studies
Cohort and Longitudinal Studies Enhancement Resources
Cohort and Longitudinal Studies Enhancement Resources (CLOSER) brings together eight world-leading longitudinal studies with participants born throughout the 20th and 21st centuries. CLOSER aims to maximise their use, value and impact both at home and abroad.
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SmartStreetSensor
The SmartStreetSensor Project is the most comprehensive study of High Street footfall patterns across Great Britain to date. It will allow us to understand how our high streets are used, how they are changing and what actually impacts footfall patterns.
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Natural environment
Environment modelling and observation
The Environmental Modelling and Observation Group combine machine learning with computational hydrodynamics for prediction of tidal surge inundation at major estuarine ports.
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Evolution and conservation of biodiversity
The Centre for Biodiversity and Environment Research (CBER) are applying modern computational technologies, including Geographic Information Systems, remote sensing, machine learning, and ecological modelling to understand where species are distributed, why they are distributed in those locations, and how biodiversity responds and feeds into environmental change.
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Improving biodiversity modelling
The Biodiversity Modelling Research Group are engaging citizen scientists to help develop algorithms that can automatically classify species using artificial intelligence.
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Mobility and morbidity data
The Institute for Environmental Design and Engineering (IEDE) have collaborated with epidemiologists at the London School of Hygiene and Tropical Medicine (LSHTM) to develop large-scale geo-referenced morbidity and mortality data with built environment information, offering valuable insights into combined effects, such as health co-benefits of greenhouse gas reduction policies in cities in Europe, China and India (PURGE); and modifying effect of the building fabric to outdoor pollution and excess temperatures (AWESOME).