Baik, J.-J., Kwak, K.-H., Park, S.-B., and Ryu, Y.-H.: Effects of Building Roof
Greening on Air Quality in Street Canyons, Atmos. Environ., 61,
48–55,, 2012.?a

Berman, M., Triki, A. R., and Blaschko, M. B.: The Lovász-Softmax Loss: A
Tractable Surrogate for the optimization of the intersection-over-union
measure in neural networks, arXiv [preprint],, 24 May 2017.?a

Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A.: Albumentations: Fast and Flexible Image Augmentations, Information, 11, 125,, 2020.?a

Castleton, H. F., Stovin, V., Beck, S. B., and Davison, J. B.: Green Roofs;
Building Energy Savings and the Potential for Retrofit, Energ. Buildings,
42, 1582–1591,, 2010.?a

Cuthbert, M. O., Rau, G., Ekström, M., O’Carroll, D., and Bates, A.: Global climate-driven trade-offs between the water retention and cooling benefits of urban greening, Nat. Commun., 13, 518,, 2022.?a

Demuzere, M., Bechtel, B., Middel, A., and Mills, G.: Mapping Europe into local
climate zones, PloS One, 14, e0214474,, 2019.?a

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.: Imagenet: A
large-scale hierarchical image database, in: 2009 IEEE Conference on Computer
Vision and Pattern Recognition, Miami, FL, USA, 2009, 248–255,, 2009.?a, b

Design for London, Greater London Authority, and London Climate Change
Partnership: Living Roofs and Walls, Greater London Authority, ISBN 978 1 84781 132 5, (last access: 28 March 2023), 2008.?a

Filazzola, A., Shrestha, N., and MacIvor, J. S.: The contribution of
constructed green infrastructure to urban biodiversity: A synthesis and
meta-analysis, J. Appl. Ecol., 56, 2131–2143,, 2019.?a

Getmapping Plc.: High Resolution (25?cm) Vertical Aerial Imagery, EDINA
Aerial Digimap Service [data set], (last access: 9 December 2022), 2020.?a

Getter, K. L., Rowe, D. B., Robertson, G. P., Cregg, B. M., and Andresen,
J. A.: Carbon Sequestration Potential of Extensive Green Roofs, Environ.
Sci. Technol., 43, 7564–7570,, 2009.?a

Gillies, S., van der Wel, C., Van den Bossche, J., Taves, M. W., Arnott, J., Ward, B. C., and others: Shapely: manipulation and analysis of geometric objects, GitHub [code], (last access: 23 March 2023), 2022.?a

Grant, G. and Gedge, D.: Living Roofs and
Walls: From Policy to Practice, European Federation of Green Roof and Green Wall Associations (EFB) and on behalf of the Greater London Authority, edited by: Blanche, C., (last
access: 23 March 2023), 2019.?a, b, c

Greater London Authority: Green roof map, Greater London Authority [data set], (last
access: 28 October 2021), 2014.?a, b, c, d, e

Greater London Authority: London Plan Annual Monitoring Report 16 2018/19,
Greater London Authority, ISBN 978-1-84781-738-9, (last access: 23 March 2023),
2021.?a, b

He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, arXiv [preprint],, 10 December 2015.?a

Hoeben, A. D. and Posch, A.: Green Roof Ecosystem Services in Various Urban
Development Types: A Case Study in Graz, Austria, Urban For. Urban Gree., 62, 127167,, 2021.?a

Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv
[preprint],, 22 December 2014.?a

Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P.: Focal loss for
dense object detection, arXiv [preprint],, 7 August 2017.?a

Livingroofs Enterprises Ltd: London borough green roof infographics and maps, (last access: 11 February 2022), 2019.?a, b, c, d

Losken, G., Ansel, W., Backhaus, T., Bartel, Y.-C., Bornholdt, H., Bott, P.,
Henze, M., Hokema, J., Kohler, M., Krupka, B. W., Mann, G., Munster, M.,
Neisser, H., Roth-Kleyer, S., Ruttensperger, S., Schenk, D., Sprenger, D.,
Upmeier, M., and Westerholt, D.: Guidelines for the planning, construction
and maintenance of green roofs, Landscape Development and Landscaping
Research Society e.V., 6th edn., Bonn, (last access: 28 March 2023), 2018.?a

Mentens, J., Raes, D., and Hermy, M.: Green roofs as a tool for solving the
rainwater runoff problem in the urbanized 21st century?, Landscape Urban
Plan., 77, 217–226,, 2006.?a

Ng, V. and Hofmann, D.: Scalable feature extraction with aerial and satellite
imagery, in: Proceedings of the 17th Python in Science Conference (SCIPY
2018), Austin, TX, USA, July 2018, 9–15,, 2018.?a, b, c, d, e

Office for National Statistics: 2011 Census geography products for England
and Wales, Office for National Statistics [data set],, last
access: 22 March 2022a.?a

Office for National Statistics: Definitions of terms and phrases used in
products and statistical outputs from the 2001 Census,, last access: 31 March 2022b.?a

Ordnance Survey (GB): OS VectorMap® Local,, last access: 21 October 2021.?a

Ozturk, O., Saritürk, B., and Seker, D. Z.: Comparison of Fully
Convolutional Networks (FCN) and U-Net for Road Segmentation from High
Resolution Imageries, International Journal of Environment and
Geoinformatics, 7, 272–279,, 2020.?a

Pastor-Pellicer, J., Zamora-Martínez, F., España-Boquera, S., and
Castro-Bleda, M. J.: F-measure as the error function to train neural
networks, in: Advances in Computational Intelligence: 12th International
Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz,
Tenerife, Spain, 12–14 June 2013, Proceedings, Part I 12, 376–384,
Springer, ISBN 978-3-642-38679-4,, 2013.?a

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen,
T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E.,
DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L.,
Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance
Deep Learning Library, arXiv [preprint],, 3 December

Peng, L. L. and Jim, C. Y.: Green-Roof Effects on Neighborhood Microclimate and
Human Thermal Sensation, Energies, 6, 598–618,, 2013.?a

QGIS Association: QGIS Geographic Information System, Version 3.22.3, QGIS Association [code], (last access: 23 March 2023), 2022.?a

Pipe, J., Ali, S., Halliwell, D., Layfield, T., et al.: The London Plan, Greater London Authority, ISBN 978-1-84781-739-6, (last access: 28 March 2023),
2021.?a, b

Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for
biomedical image segmentation, in: International Conference on Medical Image
Computing and Computer-Assisted Intervention, Munich, Germany, 5–9 October 2015, 234–241, ISBN 978-3-319-24574-4,, 2015.?a, b, c, d

Sailor, D. J., Elley, T. B., and Gibson, M.: Exploring the Building Energy
Impacts of Green Roof Design Decisions – a Modeling Study of
Buildings in Four Distinct Climates, J. Build. Phys., 35,
372–391,, 2012.?a

Shorten, C. and Khoshgoftaar, T. M.: A survey on Image Data Augmentation for Deep Learning, J. Big Data 6, 60,, 2019.?a

Simpson, C., Brousse, O., Mohajeri, N., Davies, M., and Heaviside, C.: An
Open-Source Automatic Survey of Green Roofs in London using Segmentation of
Aerial Imagery: Dataset, Zenodo [data set],, 2023.?a, b, c, d

Sproul, J., Wan, M. P., Mandel, B. H., and Rosenfeld, A. H.: Economic
Comparison of White, Green, and Black Flat Roofs in the United States,
Energ. Buildings, 71, 20–27,, 2014.?a

Stewart, I. D. and Oke, T. R.: Local climate zones for urban temperature
studies, B. Am. Meteorol. Soc., 93, 1879–1900,,

The Ecology Consultancy: Urban Greening Factor for London, (last access: 18 February 2022), 2017.?a

Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, version 3.8.12, Python Software Foundation [code], (last access: 23 March 2023), 2009.?a

Verisk Analytics, Inc.: UKBuildings, Verisk Analytics, Inc [data set], (last access: 20 December 2021), 2022.?a

Virk, G., Jansz, A., Mavrogianni, A., Mylona, A., Stocker, J., and Davies, M.:
Microclimatic effects of green and cool roofs in London and their impacts on
energy use for a typical office building, Energ. Buildings, 88, 214–228,,
2015.?a, b

Wu, A. N. and Biljecki, F.: Roofpedia: Automatic mapping of green and solar
roofs for an open roofscape registry and evaluation of urban sustainability,
Landscape Urban Plan., 214, 104167,, 2021.?a, b, c, d, e, f, g, h, i, j, k, l

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