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Zhu XX, Qiu C, Hu J, Shi Y, Wang Y, Schmitt M, Taubenböck H. The urban morphology on our planet - Global perspectives from space. Remote Sens Environ 2022; 269:112794. [PMID: 35115734 PMCID: PMC8783056 DOI: 10.1016/j.rse.2021.112794] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/23/2021] [Accepted: 11/03/2021] [Indexed: 05/15/2023]
Abstract
Urbanization is the second largest mega-trend right after climate change. Accurate measurements of urban morphological and demographic figures are at the core of many international endeavors to address issues of urbanization, such as the United Nations' call for "Sustainable Cities and Communities". In many countries - particularly developing countries -, however, this database does not yet exist. Here, we demonstrate a novel deep learning and big data analytics approach to fuse freely available global radar and multi-spectral satellite data, acquired by the Sentinel-1 and Sentinel-2 satellites. Via this approach, we created the first-ever global and quality controlled urban local climate zones classification covering all cities across the globe with a population greater than 300,000 and made it available to the community (https://doi.org/10.14459/2021mp1633461). Statistical analysis of the data quantifies a global inequality problem: approximately 40% of the area defined as compact or light/large low-rise accommodates about 60% of the total population, whereas approximately 30% of the area defined as sparsely built accommodates only about 10% of the total population. Beyond, patterns of urban morphology were discovered from the global classification map, confirming a morphologic relationship to the geographical region and related cultural heritage. We expect the open access of our dataset to encourage research on the global change process of urbanization, as a multidisciplinary crowd of researchers will use this baseline for spatial perspective in their work. In addition, it can serve as a unique dataset for stakeholders such as the United Nations to improve their spatial assessments of urbanization.
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Affiliation(s)
- Xiao Xiang Zhu
- Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
- Remote Sensing Technology Institute, German Aerospace Center, Münchener Straße 20, Weßling 82234, Germany
| | - Chunping Qiu
- Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
| | - Jingliang Hu
- Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
| | - Yilei Shi
- Department of Aerospace and Geodesy, Chair of Remote Sensing Technology, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
| | - Yuanyuan Wang
- Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
- Remote Sensing Technology Institute, German Aerospace Center, Münchener Straße 20, Weßling 82234, Germany
| | - Michael Schmitt
- Department of Aerospace and Geodesy, Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
| | - Hannes Taubenböck
- Remote Sensing Data Center, German Aerospace Center, Münchener Straße 20, Weßling 82234, Germany
- Institute for Geography and Geology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
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