Abstract
Rapid urbanization in developing countries has increased the complexity and uncertainty of urban planning, exposing limitations in traditional approaches to manage informal settlements, infrastructure deficits, housing pressure, environmental risks, and climate. The review examined emerging integration of Geographic Information Systems (GIS), Geospatial Artificial Intelligence (GeoAI), and Generative Artificial Intelligence (GenAI) in sustainable urban planning. Using a qualitative conceptual review approach, the study synthesises contemporary literature on GIS, GeoAI, planning support systems, urban informatics, AI governance, and sustainable urbanism. The review highlights the complementary roles of GIS, GeoAI, and GenAI in enhancing spatial analysis, predictive modelling, urban scenario generation, synthetic data creation, spatial visualization, participatory planning, and evidence-based decision-making. The study argues that the effectiveness of GIS– GenAI integration depends not only on technological advancement but also on institutional capacity, data quality, ethical governance, transparency, and human oversight. The paper further proposed an integrative GIS– GenAI planning framework comprising six iterative stages: data acquisition, GIS processing, GeoAI predictive analysis, GenAI-assisted scenario generation, planning interpretation and policy translation, and feedback validation. The paper contributes to emerging debates in urban informatics and smart city governance by advancing a governance-oriented conceptual framework for responsible AI-assisted urban planning and sustainable urban development. Mitigating Ineffective Building Information Modelling Execution Plans:
Authors and affiliations
- Department of Urban and Regional Planning, Ahmadu Bello University, (ABU) Zaria, Nigeria; Department of Surveying and Geoinformatics, Federal University of Technology Akure, (FUTA) Akure, Nigeria
How to cite
Tobenna Nnaemeka Nzelibe, Ifechukwu Ugochukwu Nzelibe (2026). Integrating Geospatial Intelligence and GenAI for Sustainable Urban Planning.In: SURE-Built 2026 — 2nd Global Scholarship for Sustainable Built Environment Research Conference. SureBuilt Series.
Related papers
Diagnosing Environmental Drivers of Carbon Monoxide Concentration in Kaduna Metropolis Using Interpretable Machine Learning
Reliable spatial information on urban air pollution is essential for effective environmental management and sustainable urban development. This study modelled and interpreted the spatial distribution of carbon monoxide (CO) concentration in Kaduna Metropolis, Nigeria,…
Analysis of Post-Retrofit Changes in Window Operation Behavior in Retrofitted University Buildings Using Machine Learning
Building energy retrofits are a key strategy for reducing energy consumption in the existing building stock; however, the success of this initiative is dependent on occupant behavior after the retrofit. Specifically, changes in window…
Geospatial Evidence for Sustainable Urban Development
Zambia michael.mutale@cbu.ac.zm Rapid urbanisation in developing countries poses significant threats to land cover integrity and ecological sustainability. This study assesses Land Use and Land Cover (LULC) dynamics in Kitwe District, Zambia, over a twenty-year…