# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
###### Abstract  
High-resolution Digital Surface Models (DSMs) from unmanned aerial vehicles (UAVs) imagery with accuracy better than 10 cm open new possibilities in geosciences and engineering. The accuracy of such DSMs depends on the number and distribution of ground control points (GCPs). Placing and measuring GCPs are often the most time-consuming on-site tasks in a UAV project. Safety or accessibility concerns may impede their proper placement, so either costlier techniques must be used, or a less accurate DSM is obtained. Photogrammetric blocks flown by drones with on-board receivers capable of RTK (real-time kinematic) positioning do not need GCPs, as camera stations at exposure time can be determined with cm-level accuracy, and used to georeference the block and control its deformations. This paper presents an experimental investigation on the repeatability of DSM generation from several blocks acquired with a RTK-enabled drone, where differential corrections were sent from a local master station or a network of Continuously Operating Reference Stations (CORS). Four different flights for each RTK mode were executed over a test field, according to the same flight plan. DSM generation was performed with three block control configurations: GCP only, camera stations only, and with camera stations and one GCP. The results show that irrespective of the RTK mode, the first and third configurations provide the best DSM inner consistency. The average range of the elevation discrepancies among the DSMs in such cases is about 6 cm (2.5 GSD, ground sampling density) for a 10-cm resolution DSM. Using camera stations only, the average range is almost twice as large (4.7 GSD). The average DSM accuracy, which was verified on checkpoints, turned out to be about 2.1 GSD with the first and third configurations, and 3.7 GSD with camera stations only. photogrammetry [2], i.e., of highly efficient software packages and great platform flexibility, supplies a very effective surface reconstruction tool, delivering DSM with unprecedented resolution at low cost. Photogrammetric surveys with UAVs are therefore increasingly carried out directly by research groups in many disciplines: in geology and geotechnics [3; 4; 5; 6; 7] to monitor landslides; in geomorphology [8; 9; 10; 11] to study the time evolution of landforms; in glaciology [12; 13; 14] to estimate ice volume changes and motion; and in landscape monitoring and change detection [15; 16] to support volume estimates in earthworks [17; 18]. The ease of use of inexpensive micro UAVs and their ability to operate apart from regulatory restrictions, in practically any environment, make them very handy platforms. Ground operations can be managed in most cases by just a couple of people equipped with suitable targets and geodetic GNSS (Global Navigation Satellite Systems) receivers capable of delivering cm-level accuracy. Getting rid of the sometimes awkward search for appropriate instrument setting locations, UAVs are also taking a good deal of surveys that were thought to be the preserve of terrestrial laser scanning back to photogrammetry. The smoothness of the ground operations and the high degree of automation of software packages in block orientation and DSM generation make it possible to produce high resolution Digital Surface Model (DSM) with a very limited budget and time effort. However, as [PERSON] et al. [19] point out, ensuring end product quality and block design optimisation still require a deep understanding of the photogrammetric process, which the apparently straightforward SfM workflow might hide to some extent. SfM was developed in a close range environment, where the potential for block deformations inherent to the camera network geometry in aerial photogrammetry is not so apparent; as a result, the development of means for block control was not a priority. This is perhaps why software packages with a computer vision background often apply a rigid three-dimensional (3D) similarity transformation from an arbitrary reference system to the mapping system after SfM.