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Article

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

1
Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan
2
Department of Computer Science and Engineering, University of Dhaka, Dhaka 1000, Bangladesh
3
E-government Operating Unit (UNU-EGOV), United Nations University, Guimarães 4810-515, Portugal
4
Department of Computer Science and Engineering, Data and Design Lab, University of Dhaka, Dhaka 1000, Bangladesh
5
Department of Computer Science and Engineering, Independent University, Bangladesh, Dhaka 1229, Bangladesh
6
Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken 277-856, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Agnieszka Bieda
Sustainability 2022, 14(7), 4336; https://doi.org/10.3390/su14074336
Received: 17 February 2022 / Revised: 10 March 2022 / Accepted: 25 March 2022 / Published: 6 April 2022
(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)
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. View Full-Text
Keywords: urban environment; urban morphology; mapping urbanism; urban classification; satellite images; informal area; informal statement; slum urban environment; urban morphology; mapping urbanism; urban classification; satellite images; informal area; informal statement; slum
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MDPI and ACS Style

Cheng, Q.; Zaber, M.; Rahman, A.M.; Zhang, H.; Guo, Z.; Okabe, A.; Shibasaki, R. Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality. Sustainability 2022, 14, 4336. https://doi.org/10.3390/su14074336

AMA Style

Cheng Q, Zaber M, Rahman AM, Zhang H, Guo Z, Okabe A, Shibasaki R. Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality. Sustainability. 2022; 14(7):4336. https://doi.org/10.3390/su14074336

Chicago/Turabian Style

Cheng, Qianwei, Moinul Zaber, AKM M. Rahman, Haoran Zhang, Zhiling Guo, Akiko Okabe, and Ryosuke Shibasaki. 2022. "Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality" Sustainability 14, no. 7: 4336. https://doi.org/10.3390/su14074336

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