Decarbonising domestic heating is a central challenge for the sustainability agenda - requiring fundamental changes in home heating systems and major upgrades in the thermal performance of the existing building stock to reduce energy consumption.

This research will develop city-wide indicators using a combination of urban sensing technologies, digital footprint data and machine learning (ML) models.

Through this project, we will collaborate with the UKRI-EPSRC’s Urban Flows Observatory at the University of Sheffield. The Urban Flows Observatory has developed a multi-sensor-equipped van (LiDAR, thermal, hyper-spectral) capable of scanning building frontages to produce measures of fabric, dimensions and sizes of critical elements (doors, windows, etc.) and surface temperatures. From these scans, their models can create estimates of EPC values, detailed energy models and indications of priority items for upgrading. These estimates will be produced for a set of neighbourhoods within Greater Glasgow, covering diverse property types.

We will combine the outputs from the Urban Flows scanning with a range of property-related data (Zoopla, ROS, EPC, etc.), held by UBDC Housing & Neighbourhoods research groups, and other sources such as Google Street View and aerial thermal/LiDAR surveys.

Using ML, we will build models that let us scale-up the estimates - from a sample of properties scanned by Urban Flows - to the wider city. We will also collaborate with UCL's Smart Energy Research Lab to obtain the smart meter data for the households in the target areas and potentially recruit volunteers to measure their indoor environment with the support of the sensor-enhanced housing survey project.

All these measures will help us obtain a holistic picture of the energy efficiency of housing stock in the deprived neighbourhoods and serve as a valuable data product and practise for larger-scale deployment.

Aims and objectives

This project aims to develop indicators to support efforts to raise the efficiency of our domestic building stock.

The project team will:

  • Understand the energy efficiency and energy usage of the domestic building stock in deprived areas in Greater Glasgow.
  • Generate an open urban sensing dataset covering a set of Glasgow neighbourhoods, including high-resolution, ready to use (geometric and spectral corrected), holistic thermal remotely sensed and street-level thermal/optical/hyperspectral/lidar images.
  • Create open-source code and ML models for processing high-resolution remotely sensed and street-level images. This methodology has the potential to extend to other cities.


Lead: Dr Qunshan Zhao (Urban Analytics at UBDC)

Glasgow team: Dr Qiaosi Li (PDRA in Urban Sensing, Glasgow)

Sheffield team:
Dr Hadi Arbabi (Lead)
Professor Martin Mayfield-Tulip
Dr Danielle Densley Tingley
Steve Jubb (Technician)
Dr Wil Ward (PDRA)

UCL team:
Professor Tadj Oreszczyn
Simon Elam