Performance analysis of smartphone gnss mobile applications for fit-for-purpose cadastral surveying

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Abstract The growing need for affordable and accessible geospatial tools in land administration has intensified interest in mobile GNSS applications, particularly for Fit-For-Purpose (FFP) Cadastral Mapping. This study evaluates the positional accuracy of eight selected mobile GNSS applications by comparing their coordinate outputs against reference data obtained from Differential GNSS (DGNSS). Initially, ten applications were tested under similar environmental conditions; however, two were excluded from final analysis due to persistent extreme deviations and irregular positional stability across the survey stations. Their Root Mean Square Error (RMSE) values were significantly higher than those of the other applications and exceeded the acceptable accuracy threshold for FFP Cadastral Mapping. The remaining eight apps were assessed using statistical methods, including the Kruskal-Wallis test, to determine significant differences in positional accuracy. The Mobile Topography application demonstrated the highest accuracy, with a mean deviation of 2.83 m, a 95% confidence interval of 1.57–4.09, and a standard deviation of 1.20 m. Aside from the descriptive statistics, the RMSE was computed to provide an overall measure of positional accuracy relative to the DGPS reference coordinates. Mobile Topography had the lowest RMSE of 3.03 m, falling within the acceptable FFP land administration range of not more than 5 m in rural areas. Polaris and GPS Essentials applications showed moderate reliability, while other applications recorded deviations well beyond acceptable limits, making them unsuitable for boundary-sensitive cadastral tasks. The findings affirm the potential of mobile GNSS apps as cost-effective tools for FFP mapping in resource-limited contexts while also highlighting their variability and the necessity of integrating error-mitigation strategies such as post-processing and field validation. Future research will focus on improving accuracy through hardware optimization, algorithmic corrections, and robust field methodologies to enhance the viability of mobile GNSS in FFP cadastral systems.