Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa
Monitoring and assessing the distribution of economic areas in East Africa such as low and high income neighborhoods, has typically relied on the use of structured data and traditional survey approaches for collecting information such as questionnaires, interviews and field visits. These types of s...
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| Language: | English |
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South African Institute of Electrical Engineers
2024-07-01
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| Series: | Africa Research Journal |
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| Online Access: | https://journals.uj.ac.za/index.php/SAIEE/article/view/546 |
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| author | Davy Uwizera Dr. Charles Ruranga Prof. Patrick McSharry |
| author_facet | Davy Uwizera Dr. Charles Ruranga Prof. Patrick McSharry |
| author_sort | Davy Uwizera |
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Monitoring and assessing the distribution of economic areas in East Africa such as low and high income neighborhoods, has typically relied on the use of structured data and traditional survey approaches for collecting information such as questionnaires, interviews and field visits. These types of surveys are slow, costly and prone to human error. With the digital revolution, a lot of unstructured data is generated daily that is likely to contain useful proxy data for many economic variables. In this research we focus on satellite imagery data with applications in East Africa. Recently East African cities have been developing at a fast pace by building new infrastructure and constructing innovative economic zones. Moreover with increased urban population, cities have been expanding in multiple directions affecting the overall distribution of areas with economic activity. Automatic detection and classification of these areas could be used to inform a number of policies such as land usage and could also assist with policy enforcement monitoring. On the other hand, the distribution of different economic areas in a specific city could provide proxies for various economic development variables such as income distribution and poverty metrics. In this research, we apply deep learning techniques to satellite imagery to classify and assess the distribution of various economic areas of a specific region for urban planning. By benchmarking performance against traditional machine learning models, results show that deep learning techniques yielded superior performance. ResNet50 outperforms deep models considered and achieves a classification accuracy of 98\%.
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| format | Article |
| id | doaj-art-5ef9e4fee12842ca841a564b55fa17ab |
| institution | Kabale University |
| issn | 1991-1696 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | South African Institute of Electrical Engineers |
| record_format | Article |
| series | Africa Research Journal |
| spelling | doaj-art-5ef9e4fee12842ca841a564b55fa17ab2025-01-10T08:58:38ZengSouth African Institute of Electrical EngineersAfrica Research Journal1991-16962024-07-011134Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East AfricaDavy Uwizera0Dr. Charles Ruranga1Prof. Patrick McSharrya:1:{s:5:"en_US";s:43:"Africa Center of Excellence in Data Science";}Africa Center of Excellence in Data Science (University of Rwanda) Monitoring and assessing the distribution of economic areas in East Africa such as low and high income neighborhoods, has typically relied on the use of structured data and traditional survey approaches for collecting information such as questionnaires, interviews and field visits. These types of surveys are slow, costly and prone to human error. With the digital revolution, a lot of unstructured data is generated daily that is likely to contain useful proxy data for many economic variables. In this research we focus on satellite imagery data with applications in East Africa. Recently East African cities have been developing at a fast pace by building new infrastructure and constructing innovative economic zones. Moreover with increased urban population, cities have been expanding in multiple directions affecting the overall distribution of areas with economic activity. Automatic detection and classification of these areas could be used to inform a number of policies such as land usage and could also assist with policy enforcement monitoring. On the other hand, the distribution of different economic areas in a specific city could provide proxies for various economic development variables such as income distribution and poverty metrics. In this research, we apply deep learning techniques to satellite imagery to classify and assess the distribution of various economic areas of a specific region for urban planning. By benchmarking performance against traditional machine learning models, results show that deep learning techniques yielded superior performance. ResNet50 outperforms deep models considered and achieves a classification accuracy of 98\%. https://journals.uj.ac.za/index.php/SAIEE/article/view/546classificationmonitoringurban planningrecongition |
| spellingShingle | Davy Uwizera Dr. Charles Ruranga Prof. Patrick McSharry Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa Africa Research Journal classification monitoring urban planning recongition |
| title | Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa |
| title_full | Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa |
| title_fullStr | Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa |
| title_full_unstemmed | Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa |
| title_short | Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa |
| title_sort | classifying economic areas for urban planning using deep learning and satellite imagery in east africa |
| topic | classification monitoring urban planning recongition |
| url | https://journals.uj.ac.za/index.php/SAIEE/article/view/546 |
| work_keys_str_mv | AT davyuwizera classifyingeconomicareasforurbanplanningusingdeeplearningandsatelliteimageryineastafrica AT drcharlesruranga classifyingeconomicareasforurbanplanningusingdeeplearningandsatelliteimageryineastafrica AT profpatrickmcsharry classifyingeconomicareasforurbanplanningusingdeeplearningandsatelliteimageryineastafrica |