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Article

Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World

1
Department of Computer Science and Engineering, The Independent University Bangladesh, Dhaka 1229, Bangladesh
2
Data and Design Lab, Dhaka 1229, Bangladesh
3
Department of Computer Science and Engineering, The University of Dhaka, Dhaka 1229, Bangladesh
4
E-Government Operating Unit (UNU-EGOV), United Nations University, 4810-445 Guimarães, Portugal
5
Center for Spatial Information Science, The University of Tokyo, Chiba 277-8568, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Antonio Guerrieri
Sensors 2021, 21(22), 7469; https://doi.org/10.3390/s21227469
Received: 12 October 2021 / Revised: 21 October 2021 / Accepted: 25 October 2021 / Published: 10 November 2021
(This article belongs to the Special Issue Urban Information Sensing for Sustainable Development)
This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing 70% of the urban space was used to train the models, and the remaining 30% was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of 90.0% for Dhaka, 91.5% for Nairobi, 94.75% for Jakarta, 82.0% for Guangzhou city, 94.25% for Mumbai, 91.75% for Cairo, and 96.75% for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces. View Full-Text
Keywords: urban; categorization; building; planning; structures; sustainable urban; categorization; building; planning; structures; sustainable
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MDPI and ACS Style

Rahman, A.K.M.M.; Zaber, M.; Cheng, Q.; Nayem, A.B.S.; Sarker, A.; Paul, O.; Shibasaki, R. Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World. Sensors 2021, 21, 7469. https://doi.org/10.3390/s21227469

AMA Style

Rahman AKMM, Zaber M, Cheng Q, Nayem ABS, Sarker A, Paul O, Shibasaki R. Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World. Sensors. 2021; 21(22):7469. https://doi.org/10.3390/s21227469

Chicago/Turabian Style

Rahman, A. K.M.M., Moinul Zaber, Qianwei Cheng, Abu B.S. Nayem, Anis Sarker, Ovi Paul, and Ryosuke Shibasaki. 2021. "Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World" Sensors 21, no. 22: 7469. https://doi.org/10.3390/s21227469

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