All Outputs (2)

Predicting residential building age from map data (2018)
Journal Article
Rosser, J., Boyd, D., Long, G., Zakhary, S., Mao, Y., & Robinson, D. (2019). Predicting residential building age from map data. Computers, Environment and Urban Systems, 73, 56-67. https://doi.org/10.1016/j.compenvurbsys.2018.08.004

The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction... Read More about Predicting residential building age from map data.

Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms (2018)
Journal Article
Beck, A., Long, G., Boyd, D. S., Rosser, J. F., Morley, J., Duffield, R., …Robinson, D. (2020). Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms. Environment and Planning B: Urban Analytics and City Science, 47(1), 45-64. https://doi.org/10.1177/2399808318762436

Estimating residential building energy use across large spatial extents is vital for identifying and testing effective strategies to reduce carbon emissions and improve urban sustainability. This task is underpinned by the availability of accurate mo... Read More about Automated classification metrics for energy modelling of residential buildings in the UK with open algorithms.