Monitoring land use in cities using satellite imagery and deep learning:
Over time, cities expand their physical footprint on land and new cities emerge. The shape of the built environment can affect several domains which are policy relevant, such as carbon emissions, housing affordability, infrastructure costs, and access to services. This study lays a methodological ba...
Gespeichert in:
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| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2022
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| Schriftenreihe: | OECD Regional Development Papers
no.28 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Over time, cities expand their physical footprint on land and new cities emerge. The shape of the built environment can affect several domains which are policy relevant, such as carbon emissions, housing affordability, infrastructure costs, and access to services. This study lays a methodological basis for the monitoring and consistent comparison of land use across OECD cities. An advanced form of deep learning, namely the U-Net model, is used to classify land cover and land use in EC-ESA satellite imagery for 2021. This complements conventional statistical data by monitoring large surfaces of land efficiently and in near real-time. In specific, following the availability of detailed data for model training, built-up areas in residential or business-related use are mapped and analysed for 687 European metropolitan areas, as a case application. Recent urban expansion's speed and shape are explored, as well as the potential for assessing land use in cities beyond Europe. |
| Beschreibung: | 1 Online-Ressource (49 p.) 21 x 28cm. |
| DOI: | 10.1787/dc8e85d5-en |
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| spelling | Banquet, Alexandre VerfasserIn aut Monitoring land use in cities using satellite imagery and deep learning Alexandre, Banquet ... [et al] Paris OECD Publishing 2022 1 Online-Ressource (49 p.) 21 x 28cm. Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OECD Regional Development Papers no.28 Over time, cities expand their physical footprint on land and new cities emerge. The shape of the built environment can affect several domains which are policy relevant, such as carbon emissions, housing affordability, infrastructure costs, and access to services. This study lays a methodological basis for the monitoring and consistent comparison of land use across OECD cities. An advanced form of deep learning, namely the U-Net model, is used to classify land cover and land use in EC-ESA satellite imagery for 2021. This complements conventional statistical data by monitoring large surfaces of land efficiently and in near real-time. In specific, following the availability of detailed data for model training, built-up areas in residential or business-related use are mapped and analysed for 687 European metropolitan areas, as a case application. Recent urban expansion's speed and shape are explored, as well as the potential for assessing land use in cities beyond Europe. Urban, Rural and Regional Development Delbouve, Paul MitwirkendeR ctb Daams, Michiel MitwirkendeR ctb Veneri, Paolo MitwirkendeR ctb FWS01 ZDB-13-SOC FWS_PDA_SOC https://doi.org/10.1787/dc8e85d5-en Volltext |
| spellingShingle | Banquet, Alexandre Monitoring land use in cities using satellite imagery and deep learning Urban, Rural and Regional Development |
| title | Monitoring land use in cities using satellite imagery and deep learning |
| title_auth | Monitoring land use in cities using satellite imagery and deep learning |
| title_exact_search | Monitoring land use in cities using satellite imagery and deep learning |
| title_full | Monitoring land use in cities using satellite imagery and deep learning Alexandre, Banquet ... [et al] |
| title_fullStr | Monitoring land use in cities using satellite imagery and deep learning Alexandre, Banquet ... [et al] |
| title_full_unstemmed | Monitoring land use in cities using satellite imagery and deep learning Alexandre, Banquet ... [et al] |
| title_short | Monitoring land use in cities using satellite imagery and deep learning |
| title_sort | monitoring land use in cities using satellite imagery and deep learning |
| topic | Urban, Rural and Regional Development |
| topic_facet | Urban, Rural and Regional Development |
| url | https://doi.org/10.1787/dc8e85d5-en |
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