Analysis and precision of a low cost GNSS receiver for georeference in cultural heritage and historic landscape for an HBIM environment

Abstract

For the most part, government institutions recognize that cultural heritage is a source of unparalleled expression of humanity’s footprint over time and therefore must be adequately studied, managed, and preserved. To achieve this, and especially in the case of monuments and sites of cultural-historical interest, various techniques and methodologies are employed to facilitate the acquisition of precise geometric information and geographic location. In this context, the use of Global Navigation Satellite System (GNSS) receivers enables something as fundamental as connecting the digitally reconstructed heritage model with a geographic information system located in terrestrial space. Considering that low-cost GNSS receivers are currently emerging and improving these processes and their speed of execution, this study proposes a research framework where, through measurement techniques and knowledge of the limitations of experimental methods, the precision of a low-cost GNSS receiver is used as a measurement element to establish control points in photogrammetry using Unmanned Aerial Vehicles (UAVs). The results show that the Leica Zeno FLX100 intelligent device achieves a precision of around 0.015 metres, surpassing that of other consolidated Global Positioning System (GPS) devices on the market, and that this equipment is more operational and effective for establishing Ground Control Points (GCPs) in aerial photogrammetry flights due to its speed in recording data and sending it to operators. The experimental samples were collected in two work areas: rural and urban environments. Performance was measured in terms of accuracy and feasibility in the Building Information Model (BIM) environment. In a static-urban context, the differentials between both receivers were practically identical, except on the X axis. In a dynamic-rural context, the low-cost receiver produced better results than the comparison receiver. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.