Preprints
https://doi.org/10.5194/essd-2022-259
https://doi.org/10.5194/essd-2022-259
 
10 Aug 2022
10 Aug 2022
Status: this preprint is currently under review for the journal ESSD.

An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery

Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside Charles H. Simpson et al.
  • UCL Institute for Environmental Design and Engineering, The Bartlett School of Environment Energy and Resources, University College London, London, United Kingdom

Abstract. Green roofs are roofs incorporating a deliberate layer of growing substrate and vegetation. They can reduce both indoor and outdoor temperatures, so are often presented as a strategy to reduce urban overheating, which is expected to increase due to climate change and urban growth. In addition, they could help decrease the cooling energy demand of buildings thereby contributing to energy and emissions reductions, and provide benefits to biodiversity and human well-being. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is required to estimate any effect of green roofs on temperatures (or other phenomena), but this information is currently lacking. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset. We estimate that there was 0.19 km2 of green roof in the Central Activities Zone (CAZ) of London, (0.81 km2) in Inner London, and (1.25 km2) in Greater London in the year 2019. This corresponds to 1.6 % of the total building footprint area in the CAZ, and 1.0 % in Inner London. There is a relatively higher concentration of green roofs in the City of London (the historic financial district), covering 3.1 % of the total building footprint area. The survey covers 1463 km2 of Greater London, making this the largest open automatic survey of green roofs in any city. We improve on previous studies by including more negative examples in the training data, by experimenting with different data augmentation methods, and by requiring coincidence between vector building footprints and green roof patches. This dataset will enable future work examining the distribution and potential of green roofs in London and on urban climate modelling.

Charles H. Simpson et al.

Status: open (until 05 Oct 2022)

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Charles H. Simpson et al.

Data sets

An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery: Dataset Simpson, Charles, Brousse, Oscar, Mohajeri, Nahid, Davies, Michael, & Heaviside, Clare https://doi.org/10.5281/zenodo.6861929

Charles H. Simpson et al.

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Short summary
Adding plants to the roofs of buildings can reduce both indoor and outdoor temperatures, so can reduce urban overheating which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already. We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.