DOI: 10.5281/zenodo.6861929
title: An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery: Dataset

description: This archive contains geospatial data, as well as the code used to generate the geospatial data.<br> The geospatial data consists of georeferenced polygons identifying areas which are covered by green roofs in London (GBR) generated from 2019 aerial imagery.<br> The data is described in detail in the manuscript *An Open-Source Automatic Survey of Green Roofs in London using Segmentation of Aerial Imagery*. See abstract below. <br> Archive contents:<br> `geospatial_data/green_roofs_220719.geojson` is the main result, which can be opened in any GIS program.<br> `segmentation_code` contains the Python code used to produce the segmentation from the aerial imagery.<br> `analysis_code` contains the Python code used to produce the plots and tables for the paper, as well as the OS intersection postprocessing step. GeoJSON format:<br> GeoJSON is a format for encoding geospatial data, see https://geojson.org/.<br> GeoJSON can be read using GIS programs including ArcGIS, QGIS, OGR. Input data availability:<br> Unfortunately the aerial imagery and building footprint data cannot be shared directly, as you will require the proper license. Both can be found at [Digimap](https://digimap.edina.ac.uk) provided your institution has the license. Abstract:<br> 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. 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 km^2 of green roof in the Central Activities Zone (CAZ) of London, (0.81 km^2) in Inner London, and (1.25 km^2) 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 km^2 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.