Understanding the Urban Environment from Satellite Images with New Classification Method - Focusing on Formality and Informality

Cheng, Qianwei, Zaber, Moinul, Rahman, Mahbubur, Zhang, Haoran, Guo, Zhiling, Okabe, Akiko and Shibasaki, Ryosuke, (2022). Understanding the Urban Environment from Satellite Images with New Classification Method - Focusing on Formality and Informality. Sustainability, 14(7), 2-27

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  • Sub-type Journal article
    Author Cheng, Qianwei
    Zaber, Moinul
    Rahman, Mahbubur
    Zhang, Haoran
    Guo, Zhiling
    Okabe, Akiko
    Shibasaki, Ryosuke
    Title Understanding the Urban Environment from Satellite Images with New Classification Method - Focusing on Formality and Informality
    Appearing in Sustainability   Check publisher's open access policy
    Volume 14
    Issue No. 7
    Publication Date 2022-04
    Place of Publication Online
    Publisher MDPI
    Start page 2
    End page 27
    Language eng
    Abstract Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant changes in building, managing, and responding to changes in the urban environment. The classified measurement and analysis of the urban environment in developing countries and the real-time understanding of the evolution and characteristics of the urban environment are of great significance for decision-makers to manage and plan cities more effectively and maintain the sustainability of the urban environment. Hence, a method readily applicable for the state-of-the-art computational analysis can help conceive the rapidly changing urban socio-environmental dynamics that can make the policy-making process even more informative and help monitor the changes almost in real-time. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. Firstly, the method gives a new model to scrutinize the urban environment based on the buildings and their surroundings. Secondly, the method is suited for the state-of-the-art machine learning processes that make it applicable and scalable for forecasting, analytics, or computational modeling. The paper first demonstrates the model and its applicability based on the urban environment in the developing world. The method divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. Then, we discuss the characteristics of different urban environments and the differences between the same class in different cities. We also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process. This mapping should help urban designers who are working on analyzing formality and informality in the developing world. Moreover, from the application point of view, this will provide training data sets for future deep learning algorithms and automate them, help establish databases, and significantly reduce the cost of acquiring data for urban environments that change over time. The method can become a necessary tool for decision-makers to plan sustainable urban spaces in the future to design and manage cities more effectively.
    UNBIS Thesaurus SLUMS
    URBAN ENVIRONMENT
    Keyword urban morphology
    mapping urbanism
    urban classification
    satellite images
    informal area
    informal statement
    Copyright Holder The Authors
    Copyright Year 2022
    Copyright type Creative commons
    ISSN 2071-1050
    DOI 10.3390/su14074336
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    Created: Thu, 05 May 2022, 19:54:33 JST by Mario Peixoto on behalf of UNU EGOV