Building geometry is significant in many ways such as the urban building retrofit and building stock analysis. However, the current approaches to collect building geometry data is highly limited to on-site survey or pre-registered data. The project aims to develop a method which utilises computer vision technologies to characterise building geometry data at scale with a high automation level.
This project uses MIDAS road flow counts and speed data to create data-driven traffic assignment models to be applied to national Strategic Road Network for the analysis of flow patterns.
The Active Building Centre is researching and developing innovative tools and technologies that will ensure buildings of all scales contribute to a reduction in carbon emissions and a more sustainable built environment.
The core aims of this PhD project are to understand spatiotemporal variability of air quality and determine the main drivers of air pollution in urban areas by: (a) deploying a dense network of air quality sensors based on multiple criteria, and (b) employing various air quality modelling and mapping techniques including geostatistical interpolations, statistical and dispersion modelling and data fusion approaches.
Stormwater management is a vital process for cities and towns. [...]
UK 2050 decarbonisation target requires rapid deployment of retrofit measures across over 20m existing homes. This project aims to establish and strengthen collaborations on residential retrofit activities with the Barnsley council achieved through a proof-of-concept deployment of Sheffield’s automated drive-by building assessment for the council’s social housing decarbonisation program.
This project analysed the main traffic routes in the Sheffield City Region (SCR) and compared them to the optimal routing of electric vehicles and the decreased volumes expected by the progressive adoption of alternative mobility means (e.g. electric bikes).
This project is set to deliver a roadmap for developing a regional emissions budgeting assessment framework which provides a more robust evidence base to inform decisions on emission reduction measures on a long-term basis.
This project attempts to further our current ability in identifying long-term land-use and transport planning targets by adapting recently developed models at the interface of complexity science and urban economics.
Inadequate building energy consumption data has long been a barrier for building energy research, but with the rapid progress of new technologies, including the Internet of Things (IoT), it is possible to begin addressing this issue.