Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World
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 architectu...
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oai:doaj.org-article:f4c4f4ee1818416abeee7c944fb0485f2021-11-25T18:56:42ZApplying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World10.3390/s212274691424-8220https://doaj.org/article/f4c4f4ee1818416abeee7c944fb0485f2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7469https://doaj.org/toc/1424-8220This 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>70</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the urban space was used to train the models, and the remaining <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>90.0</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Dhaka, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>91.5</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Nairobi, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>94.75</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Jakarta, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>82.0</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Guangzhou city, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>94.25</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Mumbai, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>91.75</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Cairo, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.75</mn><mo>%</mo></mrow></semantics></math></inline-formula> 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.A. K. M. Mahbubur RahmanMoinul ZaberQianwei ChengAbu Bakar Siddik NayemAnis SarkerOvi PaulRyosuke ShibasakiMDPI AGarticleurbancategorizationbuildingplanningstructuressustainableChemical technologyTP1-1185ENSensors, Vol 21, Iss 7469, p 7469 (2021) |
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urban categorization building planning structures sustainable Chemical technology TP1-1185 |
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urban categorization building planning structures sustainable Chemical technology TP1-1185 A. K. M. Mahbubur Rahman Moinul Zaber Qianwei Cheng Abu Bakar Siddik Nayem Anis Sarker Ovi Paul Ryosuke Shibasaki Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World |
| description |
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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>70</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the urban space was used to train the models, and the remaining <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>90.0</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Dhaka, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>91.5</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Nairobi, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>94.75</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Jakarta, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>82.0</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Guangzhou city, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>94.25</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Mumbai, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>91.75</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Cairo, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.75</mn><mo>%</mo></mrow></semantics></math></inline-formula> 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. |
| format |
article |
| author |
A. K. M. Mahbubur Rahman Moinul Zaber Qianwei Cheng Abu Bakar Siddik Nayem Anis Sarker Ovi Paul Ryosuke Shibasaki |
| author_facet |
A. K. M. Mahbubur Rahman Moinul Zaber Qianwei Cheng Abu Bakar Siddik Nayem Anis Sarker Ovi Paul Ryosuke Shibasaki |
| author_sort |
A. K. M. Mahbubur Rahman |
| title |
Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World |
| title_short |
Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World |
| title_full |
Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World |
| title_fullStr |
Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World |
| title_full_unstemmed |
Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World |
| title_sort |
applying state-of-the-art deep-learning methods to classify urban cities of the developing world |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/f4c4f4ee1818416abeee7c944fb0485f |
| work_keys_str_mv |
AT akmmahbuburrahman applyingstateoftheartdeeplearningmethodstoclassifyurbancitiesofthedevelopingworld AT moinulzaber applyingstateoftheartdeeplearningmethodstoclassifyurbancitiesofthedevelopingworld AT qianweicheng applyingstateoftheartdeeplearningmethodstoclassifyurbancitiesofthedevelopingworld AT abubakarsiddiknayem applyingstateoftheartdeeplearningmethodstoclassifyurbancitiesofthedevelopingworld AT anissarker applyingstateoftheartdeeplearningmethodstoclassifyurbancitiesofthedevelopingworld AT ovipaul applyingstateoftheartdeeplearningmethodstoclassifyurbancitiesofthedevelopingworld AT ryosukeshibasaki applyingstateoftheartdeeplearningmethodstoclassifyurbancitiesofthedevelopingworld |
| _version_ |
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