### Related Work  
Damage detection relies on the estimation of modal features of a bridge. Usually, the features are estimated from strain or acceleration measurements of the bridge's vibration response caused by ambient excitation [3; 4]. Natural frequencies are common for damage detection since they only require a few measurement points and are generally easy to estimate. However, it has been shown that the varying temperature of a structure can lead to a more significant frequency change than is expected from damage [5; 6]. The relationship between temperature and natural frequency is generally linear for temperatures above freezing [7; 8]. Two linear functions are applied to estimate wider temperature ranges with a discontinuity at 0 \({}^{\circ}\)C [9; 10]. However, non-linear relationships are also suggested [11; 12]. Besides the temperature influence, a change in natural frequency can also be observed for different traffic loads. [PERSON] et al. [13] find a significant influence of vehicle mass for a short-span bridge. Long-span bridges show no frequency change since the ratio of vehicle mass to bridge mass is negligible. Other popular damage detection and localisation features are mode shapes and mode shape curvatures [4]. Several measurement points along the entire structure are necessary for a reliable estimation. However, unlike natural frequencies, mode shapes are not significantly affected by temperature changes or varying traffic load [8; 14]. The damage-sensitive features can be estimated with parametric or non-parametric methods. A further distinction is made between time-domain and frequency-domain-based methods [15]. For example, time-domain based methods are Natural Excitation Techniques or Stochastic Subspace Identification (SSI). Peak-Picking and Frequency Domain Decomposition (FDD) are popular non-parametric methods in the frequency domain. However, parametric methods such as Least Squares Complex Frequency are also applied [15]. Finally, the estimated features can be used for damage detection. For example, changes in natural frequencies are detected with hypothesis tests after correcting for temperature-induced variance [7]. Autoregressive models can also identify frequency anomalies [9]. Analysis of changes in mode shapes or mode shape curvatures achieves an additional localisation of damage [4]. The bridge's vibration response is usually measured with strain sensors, displacement transducers, or accelerometers (e.g., [8; 9]). However, since installing these sensors can be complex and may also interrupt the regular operation of the bridge, remote sensing techniques are becoming more popular. For example, terrestrial laser scanners [16], vision-based systems [17], or GBRs [18] are used to monitor natural frequencies and mode shapes. GBR offers a very high precision while still retaining the possibility of measuring multiple points simultaneously [1; 19]. It is successfully applied to determine the natural frequencies and mode shapes of buildings [20; 21; 22], towers [23; 24], and bridges [25; 26]. Natural frequencies of cables can also be identified [27; 28]. The GBR results are usually validated by other sensor technology either through a direct comparison of the measurements or through a comparison of estimated modal features. A categorisation of studies, which compare and evaluate GBR with other sensor technology, is given in Table 1. For example, natural frequencies from acceleration measurements are compared to those from displacement measurements of the GBR [22; 28; 29; 30; 31]. Further comparison is achieved with mode shapes estimated from measurements of several accelerometers [32] or with the Finite Element Method (FEM) [21]. Most studies use techniques based on the frequency domain to estimate these modal features from GBR displacements. Usually, the power spectral density or the amplitude spectrum is applied for natural frequency estimation [25; 31]. Mode shapes are determined through FDD [20; 32] or SSI [26].