Applications of Geographic Information Systems (GIS) in Surveying: Technologies, Workflows, Accuracy, and Emerging Research

Geographic Information Systems (GIS) have become a central environment for integrating, analysing, visualising, and maintaining spatial information generated by contemporary surveying. Surveying practice is increasingly supported by combinations of Global Navigation Satellite Systems (GNSS), total stations, unmanned aerial vehicles (UAVs), photogrammetry, LiDAR, sonar, mobile mapping, remote sensing, and three-dimensional (3D) modelling. This review synthesises the supplied analysis of 20 research-paper entries spanning topographic and UAV surveying, cadastral mapping, mobile and crowdsourced data collection, marine surveying, hydrological and terrain modelling, construction, mining, underground utilities, augmented reality (AR), and 3D cadastre. The synthesis indicates that GIS contributes most strongly when it is used not merely as a cartographic display platform but as an integrated data-management and analytical environment connecting field observations with spatial models and operational decisions. The reviewed material also shows that reported performance is context-dependent: UAV photogrammetry can provide dense terrain information in open conditions, while vegetation, weak control geometry, sensor limitations, inconsistent coordinate reference systems, and inadequate independent validation can undermine reliability. Other recurring barriers include interoperability between GIS and Building Information Modelling (BIM), the legal status of crowdsourced cadastral information, cost and training constraints, and the dependence of AR visualisation on the positional quality of underlying utility records. A conceptual workflow and thematic taxonomy are presented, alongside comparative tables of methods, applications, benefits, and limitations. Because the supplied document is an annotated collection rather than a reproducible systematic-search dataset, the paper is framed as a critical narrative review; it does not claim exhaustive coverage or invent screening statistics. The review concludes that future progress depends on uncertainty-aware data integration, independent accuracy assessment, open and interoperable standards, fit-for-purpose 3D information models, and stronger reporting of survey design and validation.