Learning from urban form to predict building heights
Open accessAbstract
Understanding cities as complex systems, sustainable urban planning depends on reliable high-resolution data, for example of the building stock to upscale region-wide retrofit policies. For some cities and regions, these data exist in detailed 3D models based on real-world measurements. However, they are still expensive to build and maintain, a significant challenge, especially for small and medium-sized cities that are home to the majority of the European population. New methods are needed to estimate relevant building stock characteristics reliably and cost-effectively. Here, we present a machine learning based method for predicting building heights, which is based only on open-access geospatial data on urban form, such as building footprints and street networks. The method allows to predict building heights for regions where no dedicated 3D models exist currently. We train our model using building data from four European countries (France, Italy, the Netherlands, and Germany) and find that the morphology of the urban fabric surrounding a given building is highly predictive of the height of the building. A test on the German state of Brandenburg shows that our model predicts building heights with an average error well below the typical floor height (about 2.5 m), without having access to training data from Germany. Furthermore, we show that even a small amount of local height data obtained by citizens substantially improves the prediction accuracy. Our results illustrate the possibility of predicting missing data on urban infrastructure; they also underline the value of open government data and volunteered geographic information for scientific applications, such as contextual but scalable strategies to mitigate climate change.
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Building heights can be predicted without elevation data from real estate records, census data and features characterizing the neighborhood of each building.
“Recent research showed that heights can be predicted even without such elevation data, as demonstrated by [77,537], who predicted these from real estate records, census data and features characterizing the neighborhood of each building.”
From Tackling climate change with machine learning (2023) · cites this paper as [537]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
Self-citation
- Authors
- Milojevic-Dupont, N., Hans, N., Kaack, L. H., Zumwald, M., Andrieux, F., de Barros Soares, D., Lohrey, S., Pichler, P.-P., & Creutzig, F.
- Affiliations
- Mercator Research Institute on Global Commons and Climate Change (DE)
- Humboldt-Universität zu Berlin (DE)
- ETH Zurich (CH)
- Potsdam Institute for Climate Impact Research (DE)
- Corresponding author
- Milojevic-Dupont, Nikola
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-12-09
- Volume
- 15
- Issue
- 12
- Pages
- e0242010
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7725312
- OpenAlex
- W3111868370
- Cited by
- 93
- References
- 48
- Citations by year
- 2026: 16 · 2025: 13 · 2024: 19 · 2023: 11 · 2022: 26 · 2021: 8
- FWCI
- 5.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OPUS 4 (Zuse Institute Berlin) (submittedVersion)
- Publication Database PIK (Potsdam Institute for Climate Impact Research (PIK)) (submittedVersion, cc-by)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Topics
- Physical Sciences › Engineering › Building and Construction › 3D Modeling in Geospatial Applications
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Keywords
- urban form, building stock, urban morphology, building footprints, street networks, open geospatial data, volunteered geographic information, citizen data, 3D city model, climate change mitigation, sustainable urban planning, small and medium-sized cities
- MeSH terms
- Machine Learning, Sustainable Development, Cities, City Planning, Europe, Forecasting
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