# GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring
###### Abstract
The simplest way to overcome the GNSS restriction is the integration of GNSS with other geodetic or non-geodetic equipment and measurements. Geodetic science has developed many methods to measure structure deformation in given directions. Today, we deal with different terrestrial geodetic measurements, satellite measurements, and photogrammetric technologies and measurements. Terrestrial geodetic measurements are the most widespread [2; 3]. There is no point in discussing these methods in detail as anyone may find their description in the geodetic literature. Moreover, these methods were the first ones applied for deformation monitoring and, consequently, are well studied. The primary geodetic methods are spirit and hydrostatic/dynamic leveling for vertical displacements, and total stations, including image-assisted total stations [4; 5], for spatial displacement determination. Other kinds of terrestrial geodetic methods are terrestrial laser scanning [6; 7], depth cameras [8], and ground-based radar interferometry [9]. The papers [10; 11] further develop InSAR technology. Publication [8] is focused on comparing a depth camera and terrestrial laser scanner to estimate structural deflection. The comprehensive analysis of the ground-based radar interferometry is given in [11], where the structural monitoring and damage assessment of constructions are considered as the GIS integration of differential InSAR measurements, geological investigation, historical surveys, and 3D modeling. However, those technologies are laborious, require skilled personnel, and are hard to automate. Especially critical for geodetic methods is the question of the observation frequency. It is clear that leveling, terrestrial laser scanning, or ground-based radar interferometry cannot provide more than one observation epoch per day. That is why pure geodetic methods are being used as an additional information source in combination with other methods. Notable success has been achieved in the joint application of GNSS with terrestrial geodetic measurements and sensors. GNSS was applied for monitoring tasks for the first time more than twenty years ago [12; 13; 14; 15]. Despite the high observation frequency and relatively high accuracy, the GNSS application is restricted to the number of observation points (i.e., it is impossible to install the necessary number of GNSS antennas on a structure), observation sites (i.e., the necessity to have a relatively open sky for satellites), and sophisticated processing algorithms. The GNSS was combined with other sensors to overcome these shortcomings to detect displacements in many points, e.g., accelerometers, hydrostatic levels, inclinometers, etc. [16; 17; 18; 19]. The study [17] presents a good case of GNSS integration with low-cost accelerometers, but the solution is not a monitoring system. Paper [19] is a different approach to integrating GNSS with other devices. GNSS aids non-overlapping images from two cameras to determine the three-dimensional displacements of high-rise buildings. This case is an example of vision-based technology assistance. The main branch of such technologies is photogrammetry, where the image is the primary data source. Here and below, only close-range photogrammetry is considered. Close-range photogrammetry can be separated into terrestrial and aerial cases. Terrestrial photogrammetry has been known since its invention, whereas close-range aerial photogrammetry took its place by deploying effective and cheap unmanned aerial vehicles (UAVs). This is why the latter is often called UAV/UAS photogrammetry. On the one hand, its traditional concept applies close-range photogrammetry for structure deformation monitoring. On the other hand, the new achievements in computer vision technologies and digital image processing [20; 21; 22; 23; 24] have transformed classical photogrammetry into digital photogrammetry, where the opportunities for measurement automatization have risen significantly.