Enhancing Local Geoid Modelling for Urban Built Environment Applications using Integrated GNSS and Levelling Techniques in Lagos Island, Nigeria

Inconsistent local height reference systems continue to pose a critical problem for spatial analyses and disaster management, particularly in coastal cities. Vertical reference is vital for city planning, flood analysis, and engineering construction on Lagos Island, where coastal hazards and a lack of infrastructure, combined with rapid urbanisation, make the environment highly susceptible to frequent and devastating disasters. In this study, a high-accuracy local geoid model was computed for the study area (Lagos Island). The orthometric heights were obtained using spirit levelling, while the ellipsoidal heights were derived using a Promark2 GNSS receiver. A total of eighty (80) points that had both orthometric and ellipsoidal heights were used for model fitting, and 10 other control points that were not used for model fitting were used to evaluate the model. The geoid undulations were computed using a 5-parameter surface transformation model by solving a system of equations using the least squares method. The geoid undulation values ranged from +0.569 to -0.353 m, indicating that the study area is generally low-lying and coastal. An RMSE of 0.2156m and a standard deviation of the residuals of 0.2126m were found during validation. On removal of the two rogue points A6 and A7, the RMSE and the standard deviation of the residuals were reduced to 0.1126m and 0.0906, respectively. General decimeter-level accuracy was achieved across most of the survey network. The local geoid model derived for Lagos Island was found to be accurate. The integration of traditional and modern GNSS methods can establish a robust methodological knowledge system for coastal city planning. Beyond its immediate engineering value, the resulting model demonstrates how geodetic techniques could be repositioned as a basis for emerging predictive spatial-analytics and digital twin applications in disaster management in coastal areas, aligning this study with the wider sustainable built-environment research. Managing the Construction Workforce in the Age of AI: