2.1. GNSS
The Global Positioning System (GPS) constitutes the first satellite-positioning system, which was developed by the US Department of Defense in 1973. In 1993, the system became operational, while in 2000, GPS’s data was fully publicly available. Since then, other satellite positioning systems have been launched, such as GLONASS, BEIDOU, GALILEO, etc. The term GNSS encompasses all measurements of these compatible systems, providing to users dense time-series of precise positions for long intervals.
The first attempts to monitor infrastructure using GNSS are traced back to 1988 and 1995, through the collection of high-precision geodetic measurements over dams [
23,
24]. The approaches provided continuous measurements of displacement over time, contributing to the analysis of the dam deformation with accuracy of approximately 0.5 cm. After these first successful attempts, several researchers dealt with GNSS-based dam monitoring and various processing methodologies were developed towards the automation of the monitoring procedure and the improvement of the achieved accuracy at millimeter-scale [
9,
25,
26,
27,
28]. In particular, a continuous GNSS monitoring system was proposed for the monitoring of dam deformation and the subsequent investigation of the role of the water level within the reservoir. The system was based on the creation of a permanent GNSS network, which contributed to an excellent performance (millimetric-scale) monitoring of the different parts of the dam in order to obtain useful information for dam deformation that cannot be retrieved through conventional GNSS methods [
29]. In a retrospective study, dam horizontal displacements were determined using terrestrial measurements and GNSS-based techniques, taking into account 25 reference stations that were established outside of the dam’s deformation zone [
30]. On the other hand, real-time kinematic (RTK) GNSS sensors have also been utilized for high-precision deformation monitoring [
26,
31,
32]. In other studies, displacements, resulting from GNSS processing, were analyzed and compared with pendulum data with sub-millimeter standard deviation, however the integrated data should be in the same reference system [
33]. A typical dam-monitoring survey using GNSS receiver is displayed in
Figure 3.
Moreover, GNSS sensors are widely used in the monitoring and maintenance of bridges. In fact, the first bridge-monitoring campaign was performed in 1996, with remarkable results, through the installation of 1 Hz GPS RTK receivers onto the Humber Bridge parapet, while various surveys on other bridges were subsequently conducted, utilizing either carrier phase/pseudorange GPS receivers or GNSS receivers [
34,
35,
36]. However, RTK technique is still facing many shortcomings such as the non-guaranteed quality of base station observation, the correct placement of the reference station in a stable location, etc. These shortcomings can be overcome by utilizing RTK network technology, which is a real-time, high-precision positioning approach, based on carrier phase. Network-based RTK uses a regional, more reliable network error correction model, derived from the processing of data from continuous operation reference stations. In this way, dynamic bridge displacements are provided with adequate efficiency and lower operating costs [
37,
38]. Another, GNSS-based monitoring technology is precise point position (PPP), which performs accurate positioning using a single receiver and without the need of a reference station [
39]. PPP was focused on the estimation of fixed position; however, it has been a research hotspot in recent years with several published studies concerning displacement monitoring [
40,
41].
At the same time, other researchers examined the exploitation of a fully low-cost monitoring system, both in terms of hardware and software. Thus, several combinations of GNSS receivers and antennas as well as various sets of processing parameters were evaluated, while data processing was executed in open source and commercial software, taking into account different experimental bridge monitoring scenarios [
42,
43,
44,
45]. Moreover, many studies have focused on eliminating GPS measurement errors through the application of common non-linear methods during GPS positioning adjustment such as nonlinear adaptive-recursive least square, extended Kalman filter or wavelet principal component analysis [
45,
46]. The wavelet principal component method proved to be the most satisfactory solution for the improvement of the quality of high-frequency GPS time series observations [
46].
As the automation of the infrastructure-monitoring procedure evolves and the need to record and predict any potential deformation grows, soft computing algorithms and soft computing techniques are constantly gaining momentum. Therefore, artificial neural networks and adaptive neuro-fuzzy inference systems have been successfully used as further processing steps in GNSS observations for displacement prediction and risk assessment [
47,
48].
Finally, it is worth mentioning that an integrated methodology, consisting of GNSS measurements and other types of remote-sensing data, has been proposed and sufficiently implemented for evaluation of the deformation on buildings, located in the Italian pre-Alps [
49].
2.2. SAR
Infrastructure monitoring using SAR imagery constitutes a common practice that goes beyond the growing monitoring requirements (large-scale monitoring, regular deformation measurements, cost efficiency, etc.). In this context, SAR data and interferometric techniques are able to estimate the deformation of multiple parts of a given infrastructure with sub-centimeter accuracy.
In more detail, conventional dam-monitoring methodologies are time consuming, usually expensive, and high-demanding. On the other hand, SAR Interferometry (InSAR) is able to provide accurate and dense deformation measurements even in slow-moving areas. The first dam deformation studies were based on the utilization of ERS-1/2 and ENVISAT imagery. In particular, almost two hundred interferograms were generated from the processing of the aforementioned data through the Small BAseline Subset (SBAS) InSAR technique, aiming at analyzing the deformation behavior of the Genzano di Lucania Dam in Italy [
50]. In retrospective studies, long-term time series of ERS-1/2 and ENVISAT data were supplemented by imagery obtained by either TerraSAR-X or Sentinel-1 mission in order to analyze and understand the deformation process and dam’s behavior mechanisms [
51,
52]. In fact, the launch of the Sentinel-1 mission breathed new life into dam-deformation monitoring by providing denser time-series of freely available observations. Therefore, a rapid growth in publications concerning the monitoring of dam stability and dam maintenance using Sentinel-1 imagery has been observed in recent years [
53,
54,
55,
56]. InSAR deformation measurements derived from Sentinel-1 data have proven to be consistent with the in-situ measurements with RMSE at about 2 mm/year [
57]. In addition, high-resolution SAR data (COSMO-SkyMed, TerraSAR-X/TanDEM-X) have been effectively utilized for dam monitoring, aiming at achieving better results due to the higher resolution of the missions [
58,
59]. SAR imagery, processing techniques and software that were utilized in the aforementioned studies are displayed in
Table 2.
Moreover, large-scale surface deformation measurements derived using radar data and interferometric techniques—especially persistent scatterer interferometry (PSI)—have been widely used for the detection of damage to bridges in order to mitigate the risk and plan properly their future maintenance. Specifically, 61 COSMO-SkyMed images were submitted to PSI processing to identify damage in a bridge from 2011 to 2017, and damage causes were then analyzed [
60]. Corresponding high-resolution radar data, obtained by X-band missions (COSMO-SkyMed, TerraSAR-X) have been utilized either solely or in combination with C-band radar images for the assessment of the stability and consequent structural health of bridges [
61,
62,
63]. Recently, bridge-monitoring studies have been based on the acquisition of Sentinel-1 data due to their short revisit time, which allows the provision of almost real-time deformation measurements (vertical and horizontal) along the construction [
64,
65]. In this framework, the high-temporal resolution Sentinel-1 imagery is combined with archived ERS/ENVISAT data and high-spatial resolution COSMO-SkyMed images for the long-term monitoring (more than twenty years) of the displacement patterns and the investigation of viaduct stability [
66]. Despite the differences in the spatial resolution of the datasets, the evaluation and analysis of the derived displacements demonstrated a spatio-temporal consistency of the patterns, which was compared with in-situ measurements. Additionally, other researchers have tried to improve the accuracy of the results of the interferometric procedure by applying various approaches such as: (a) the use of the long–short baseline iteration method along with the LLL lattice reduction algorithm for the reduction of ambiguities during phase unwrapping [
67], or (b) the utilization of seasonal variation models for the post-analysis of PSI displacements [
68]. Some indicative bridge-monitoring studies, based on SAR imagery and interferometric techniques, are presented in
Table 3. At the same time, pioneering works has been carried out on how to tackle thermal dilation phenomena in order to derive higher-quality deformation velocity maps. In this context, high-resolution SAR data, obtained by X-band sensors have been exploited to isolate the thermal expansion parameter occurred over viaduct from the observed deformation pattern [
69,
70]. While similar approaches have been adopted for the analysis of the static structure of bridges and the estimation of high-precision topography [
71,
72].
Ensuring the safety of large-scale transportation infrastructure, such as road network and railways, constitutes a critical issue for the public and an important area of research for many scientists. In particular, high-resolution SAR data, acquired by TerraSAR-X or COSMOS-skymed mission have effectively applied in the analysis of the long-term deformation of large-scale linear infrastructure, consisting of highways and railways [
73,
74,
75,
76,
77]. Furthermore, a method for the quantification and characterization of the seasonal surface deformation of highways was proposed [
78]. The method was based on the estimation of the surface deformation and the subsequently calculation of seasonal indices (i.e., deformation concentration degree, deformation concentration period). In other studies, surface deformation of transportation infrastructure, derived by C-band SAR data, was correlated with the local soil characteristics and geological setting in order to obtain a more comprehensive understanding of the risk [
79,
80,
81]. Moreover, it has been demonstrated that the synergistic use of PSI and LiDAR measurements contributes to the optimization of the deformation monitoring procedure as well as the improvement of the quality of the 3D geolocation of the permanent scatterers [
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
83,
84]. Recently, more sophisticated transportation infrastructure monitoring approaches have been developed to: (a) fully automate the detection of displacements and/or potential warnings over large-scale transport networks and (b) create effective decision-making tools [
85,
86]. These approaches combined interferometric deformation measurements with GIS method and/or machine learning algorithms (i.e., regression tree, support vector machine, boosted regression trees, random forest). An overview of SAR data, processing techniques and software, which are utilized in some indicative transportation infrastructure monitoring studies, is presented in
Table 4.
It is worth mentioning that SAR imagery and interferometric techniques have also been used for the monitoring of the deformation of buildings [
87,
88]. In this context, various processing methodologies were examined, including stable point network approach, interferometric point target analysis, hierarchical clustering methods [
89,
90,
91].
2.3. LiDAR
The latest advances in sensor technology and data-processing capabilities have highlighted LiDAR as a promising technique for high-precision three-dimensional mapping with a wide range of applications. The use of LiDAR technology for infrastructure management and maintenance purposes has grown significantly over the last decade, due to the ability to collect dense 3D representations of the investigated objective, at high speed and low cost. Thus, several studies have already been published, dealing with the exploitation of LiDAR data for infrastructure applications [
11,
12,
13].
In particular, terrestrial laser scanning (TLS) data have been effectively utilized to create 3D bridge reconstruction models [
92,
93]. In fact, methodologies have been proposed concerning the evaluation of the generated point clouds in terms of quality and geometric accuracy in order to produce higher quality 3D bridge models [
93]. In retrospective studies, TLS imagery were used along with other multi-source data (i.e., data obtained by mobile mapping systems, photogrammetric data) or semi-automated algorithm-based approaches to create detailed informative models of the efficient building information modeling (BIM) and structural health monitoring (SHM) [
94,
95]. Furthermore, the specific type of remote-sensing data has proven its high potential in the detection and evaluation of cracks in concrete bridges [
60,
96]. On the other hand, other researchers have attempted to measure the vertical seasonal displacements of steel bridges using TLS data and a strictly defined processing methodology [
97]. In more detail, the approach is based on the differentiation of high-resolution TLS point clouds as well as the high-pression georeferencing of a defined control network. Lately, innovative methodologies, formulated on deep learning algorithms or octree algorithms have been effectively utilized for the robust and automated recognition of bridge components as well as the creation of a shape information model and the subsequent monitoring of shape deformation [
98,
99]. At the same time, data acquired by LiDAR sensors, combined with 4D design models, were used for the successful and rapid identification of workflow discrepancies during an on-going construction of a bridge [
100].
Moreover, numerous studies have demonstrated that laser-scanning technology is capable of detection millimetric-scale deformation within monitoring and management projects of road and rail transportation networks [
12,
13]. In this framework, mobile laser scanners have been efficiently used in the extraction of road edges and the detection of road curbs, contributing to the assessment of the risk safety along roads as well as the maintenance of the transportation networks [
101,
102,
103]. In fact, the processing of corresponding data provided useful information on roadways, which are located in a complex environment, and they are characterized by heterogeneity and lack of a raised curb [
104]. In addition, LiDAR data have been correlated with roughness descriptors for the automated segmentation and classification of asphalt and stone pavements [
105]. Other studies focused on improving the processing quality or the achievable accuracy of the derived point clouds. Specifically, methodologies both for the alignment of the lanes of highways as well as the correction of navigation accuracy and point cloud quality, based on the extraction of feature information, were proposed [
106,
107]. Regarding railway industry, TLS data managed to collect significant amount of surface information in a short time, while monitoring the progress of the renovation of a railway structure [
108]. Meanwhile, methods have been developed for the identification of railway assets (rail tracks, contact cables, catenary cables, etc.) on point clouds, derived from LiDAR sensors [
109]. The approach showed average recognition accuracy greater than 95%.
Although there are not enough studies due to the great dependence on conventional monitoring practices, LiDAR sensors have been utilized to monitor the deformation of multi-type dams (earth-rock, concrete-faced rockfill), allowing the determination of displacements in both horizontal and vertical directions [
110,
111,
112]. In particular, the exploitation of TLS data is sufficiently accurate for the inventory of a dam under construction and the deformation analysis on the upstream face, which is independent of the position uncertainties of the scanner due to the high precision of alignment of the repetitive point clouds [
113]. Equally few are the studies dealing with the use of laser scanning technology for the assessment of structural deformation in archaeological sites [
114,
115]. Specifically, the processing of TLS data obtained over archaeological monuments allowed the identification of displacements and the evaluation of the mechanisms of instability, providing an immediate warning of possible failures and a useful tool to support preservation activities [
114]. A list of studies associated with the use of LiDAR data for different infrastructure monitoring purposes is displayed in
Table 5.
2.4. UAV
The demand for health assessment and the monitoring of infrastructure—consisting of bridges, road and railway networks, dams, etc.—is constantly growing over time. The conventional monitoring techniques have serious disadvantages such as such as inadequate evaluation, poor accessibility and high cost. Novel remote-sensing methodologies overcome these limitations and allow the extraction of robust and operational information on infrastructure management. Thus, several studies have adopted UAVs for multiple infrastructure monitoring purposes [
14,
15,
16,
17,
18]. Some indicative studies dealing with the use of UAVs for infrastructure-monitoring applications are listed in
Table 6.
In more detail, the first attempts focused on the creation of three-dimensional reconstruction models of bridges through the processing of the obtained UAV imagery using structure from motion (SfM) algorithm [
93,
116]. In other studies, UAV data were used either in combination with measurements obtained by other remote-sensing sensors or in conjunction with the 3D digital image correlation methods in order to monitor and assess the structural health of bridges [
48,
117]. In fact, the methodology proposed by [
48] is quite similar to that of the “PROION” project. However, the main difference between them is that the current work (i.e., “PROION”) utilizes diverse remote-sensing data (UAV, TLS, SAR, GNSS) for the monitoring of different types of infrastructure, while the previous one is more orientated to the assessment of the structural behavior of bridges. Added to this, there are some dissimilarities regarding the in situ instrumentation.
Furthermore, UAV imagery along with object-based image analysis (OBIA) have been effectively utilized for the identification and quantification (width, length, extension) of deteriorated areas in concrete bridges, contributing to the monitoring of deterioration’s evolution and the appropriate execution of maintenance measures [
118]. Meanwhile, numerous different UAVs, along with a variety of soft computing algorithms, have been proposed for the efficient crack detection and damage quantification and assessment [
119,
120,
121,
122].
Moreover, UAVs are able to generate detailed 3D reconstruction models of road surface, to determine successful the road conditions as well as to assess immediately the road degradation [
123,
124,
125]. Innovative soft computing algorithms and soft computing have been applied to UAV imagery to effectively predict road cracks and understand the current damage status of transportation networks [
126,
127]. Additionally, UAV photogrammetry has proven to be sufficient to monitor surface changes and operation progress during the construction of expressways [
128]. Relevant studies have been carried out for the evaluation of railway conditions and the monitoring of rail tracks, within the context of infrastructure hazard mitigation [
129,
130].
Equally important is the contribution of UAV sensors to the mapping and evaluation of structural damage of buildings. Therefore, high-resolution oblique UAV images have been utilized along with the OBIA technique for the identification and assessment of damage to building facades and roofs [
131]. Remarkable progress has been made in the last decade in UAV technology, soft computing and image processing, leading to robust and sophisticated infrastructure monitoring solutions. Deep learning algorithms have proven to be particularly capable of detecting cracks and assessing the structural health of buildings in a rapid and high-precision manner within UAV imagery [
132,
133,
134].