By James Otobo
As Nigeria faces rapid urban expansion and growing housing demands, the need for efficient resource management and sustainable planning has become more pressing. With infrastructure sometimes struggling to keep pace with growth, the government has sought data-driven solutions to make proactive, informed decisions. Recently featured for his work, Akinsuyi Samson Ayomide, a Machine Learning Scientist II at the Ogun State Ministry of Housing, is working to address these challenges through artificial intelligence.
Within his first month at the ministry, Samson introduced machine learning models that predict housing needs and track urban development trends, improving resource allocation efficiency by 15%. His models, using neural networks and data analytics, allow officials to anticipate demands and direct resources where they are most needed. This approach provides data-backed insights into areas of rapid growth and regions that may require additional infrastructure.
A core aspect of Samson’s work is his use of image recognition models powered by Convolutional and Recurrent Neural Networks (CNNs and RNNs). By analyzing satellite imagery and other geospatial data, he helps government officials gain a clearer understanding of urban expansion, land use, and population density. These insights enable the ministry to make more informed decisions about zoning and infrastructure investments that support balanced growth across the state.
In an interview last week, Samson shared his thoughts on the impact of his work, saying, “I believe technology should serve people and address real needs. This project goes beyond data—it’s about planning for a sustainable city that can support everyone.”
Through his practical and data-driven approach, Samson has introduced a model for incorporating AI into public service that other states in Nigeria are beginning to consider. His contributions have improved operational efficiency within the Ministry of Housing and established an approach to integrating AI that others in the public sector may find useful.
In addition to his technical work, Samson is committed to presenting AI-driven insights in a clear, accessible way for policymakers. His ability to simplify complex outputs has made it easier for officials to act on the findings, helping them better respond to challenges in infrastructure and housing. By ensuring that these insights are understandable and relevant, he has helped bridge the gap between technology and governance in a practical, straightforward manner.
Looking ahead, Samson plans to expand the data sources used in his models, integrating more localized information to enhance predictions. With data from IoT sensors and mobile networks, he envisions a responsive planning system that can adapt to shifts in population density, traffic, and housing needs in real-time. While there is more work to be done, Samson’s approach represents a modest but valuable step toward data-informed urban planning that aims to benefit the people and communities it serves.
James Otobo is a social activist and writes from Abuja