Home / Current Issue / Paper 1714871
Comparative Assessment of Machine Learning Algorithms in Characterizing Urban Sprawl in Benin City
Subject area: Science,Engineering and Technology · Area of research: GIS and Remote Sensing
Abstract
Urban growth across Sub-Saharan Africa is occurring at an unprecedented pace, often without adequate planning control. In cities such as Benin City, Nigeria, this expansion is reshaping landscapes, accelerating forest loss, and increasing environmental vulnerability. This study comparatively assesses two widely used machine learning algorithms: Support Vector Machine (SVM) and Random Forest (RF), in mapping and characterizing urban sprawl using multi-temporal Landsat imagery from 2014, 2019, and 2024.Supervised classifications were performed to generate land use/land cover (LULC) maps, and classification accuracy was evaluated using confusion matrices, Overall Accuracy (OA), Kappa statistics, and class-level accuracies. Post-classification change detection, land cover transition analysis, transportation corridor buffering, and Digital Elevation Model (DEM) assessment were employed to examine spatial growth patterns and their environmental implications. The results show that built up land increased dramatically from approximately 13% to 18% in 2014 to about 35% to 36% in 2024. Conversely, forest cover declined from over 47% to 56% to less than 12% to 16% during the same period. Nearly 79% of new urban development occurred within 1 km of major road corridors, while about 68% took place in medium elevation zones. Although both algorithms performed strongly, SVM demonstrated more consistent and stable classification accuracy across years. The findings highlight that the scale of urban transformation in Benin City and underscore the value of machine learning based monitoring in supporting sustainable urban planning and environmental management.
References
[1] Alsharif, A. A. A., and Pradhan, B. (2014). Urban sprawl analysis of Tripoli metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149–163.
[2] Belgiu, M., and Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.
[3] Chaturvedi, V. and de Vries, W. T. (2021). Machine Learning Algorithms for Urban Land Use Planning: A Review. Urban Science, 5(3), 68.
[4] Iduseri, E. O., Awoniran, D. R., Izunobi, J. U., Abdulrasheed, T. H., Abbas, I. I., and Olawole, M. O. (2024). Sustainable development goals, governance and assessment of the impact of urban growth on vegetation cover in Benin City, Nigeria, using land-use–land-cover change trajectories. Discover Sustainability, 5(1).
[5] Krishnaveni, M., and Anilkumar, P. P. (2020). Urban sprawl analysis using remote sensing and GIS: A review. Environmental Challenges.
[6] Mao, W., Lu, D., Hou, L., Liu, X., & Yue, W. (2020). Comparison of machine-learning methods for urban land-use mapping in Hangzhou City, China. Remote Sensing, 12(17).
[7] Odjugo, P. A. O., Enaruvbe, G. O., and Isibor, H. O. (2015). Geospatial approach to spatio- temporal pattern of urban growth in Benin City, Nigeria. African Journal of Environmental Science and Technology, 9(3), 166–175.
[8] Odeyale, T. O. (2023). Reflections on the Impact of Urban Sprawl on the Architecture of the City: Lessons from Benin City, Nigeria. Forum Geografi, 37(1).
[9] Okhakhu, P. A. (2018). Climate variability and urban environmental change in southern Nigeria. Nigerian Journal of Environmental Sciences, 12(2), 101–115.
[10] Wang, J., Wang, Y., Li, G., & Qi, Z. (2024). Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications. Agronomy, 14(9).
How to cite this paper
@article{1714871,
author = {Igbafen, M. O., Igbokwe, J. I., Achionye, C. I.},
title = {Comparative Assessment of Machine Learning Algorithms in Characterizing Urban Sprawl in Benin City},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {1035-1040},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714871.pdf},
abstract = {Urban growth across Sub-Saharan Africa is occurring at an unprecedented pace, often without adequate planning control. In cities such as Benin City, Nigeria, this expansion is reshaping landscapes, accelerating forest loss, and increasing environmental vulnerability. This study comparatively assesses two widely used machine learning algorithms: Support Vector Machine (SVM) and Random Forest (RF), in mapping and characterizing urban sprawl using multi-temporal Landsat imagery from 2014, 2019, and 2024.Supervised classifications were performed to generate land use/land cover (LULC) maps, and classification accuracy was evaluated using confusion matrices, Overall Accuracy (OA), Kappa statistics, and class-level accuracies. Post-classification change detection, land cover transition analysis, transportation corridor buffering, and Digital Elevation Model (DEM) assessment were employed to examine spatial growth patterns and their environmental implications. The results show that built up land increased dramatically from approximately 13% to 18% in 2014 to about 35% to 36% in 2024. Conversely, forest cover declined from over 47% to 56% to less than 12% to 16% during the same period. Nearly 79% of new urban development occurred within 1 km of major road corridors, while about 68% took place in medium elevation zones. Although both algorithms performed strongly, SVM demonstrated more consistent and stable classification accuracy across years. The findings highlight that the scale of urban transformation in Benin City and underscore the value of machine learning based monitoring in supporting sustainable urban planning and environmental management.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1714871}
}