UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art

Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research.


Introduction
In the past decade, substantial technological progress has been recorded in the manufacturing of unmanned aerial platforms and affordable lightweight active and passive sensing devices, and the integration of microelectronics. Benefiting from the above, remotely controlled integrated sensing systems, which do not require an on-board crew, are being continuously miniaturized and have become widely accessible for commercial use. The development of integrated unmanned aircraft system (UAS)-based solutions is increasingly providing researchers with means to capture remote sensing data for archaeological applications, at spectral, spatial, and temporal resolutions not achievable with satellite or manned systems. UAS-based data collection is consistently becoming cost-effective given the unprecedented increase of precision and accuracy, and the ever-present capacity to cover vast, often inaccessible historical sites, of varying topographical characteristics with shorter flights and with less time-consuming acquisition planning. Thus, implementations of UAS for archaeology aim to fill in the existing gap between satellite/airborne sensing and terrestrial archaeological investigations.
The scope and spatiotemporal characteristics of an archaeological application are determinant for the optimal combination of platforms, sensing payloads, and processing techniques. This paper aims to present a comprehensive survey of the archaeological UAS-based remote sensing approaches reported in recent literature and gives a detailed account of the current state-of-the-art on relevant sensors, integrated payloads, and aerial platforms. The conducted research tracks the integration of technological advancements made during the last decade-on uncrewed platforms, lightweight International Society of Photogrammetry and Remote Sensing has published 9 articles (~13.2% of the total number of analyzed publications), under the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, and ISPRS International Journal of Geo-Information. The WILEY journal Archaeological Prospection published the largest number of articles (8), while Elsevier's Journal of Archaeological Science: Reports published 5 and MDPI's Remote Sensing published 4. The trend in publication had increased each year steadily, until 2018, with the maximum growth in the number of articles published observed between 2017 and 2018 ( Figure 1). Citation information derived from Google Scholar shows that the average number of times each publication has been cited is 9.2, with a minimum of 0, a maximum of 84 ([55] published in Journal of Archaeological Science), and a median of 3. Archaeological prospection related applications accounted for ~77% of the documented studies, while much smaller numbers were observed for the other types of applications, ~23% for historical terrain visualization, ~13% for archaeogeography, and ~6% for site monitoring applications ( Figure  2). Nevertheless, the studies included in the meta-analysis covered a wide range of archaeological contexts and, broad geographic extent.    Figure 1). Citation information derived from Google Scholar shows that the average number of times each publication has been cited is 9.2, with a minimum of 0, a maximum of 84 ([55] published in Journal of Archaeological Science), and a median of 3. Archaeological prospection related applications accounted for ~77% of the documented studies, while much smaller numbers were observed for the other types of applications, ~23% for historical terrain visualization, ~13% for archaeogeography, and ~6% for site monitoring applications ( Figure  2). Nevertheless, the studies included in the meta-analysis covered a wide range of archaeological contexts and, broad geographic extent.  Study locations were mapped from the 68 publications, and it was found that UAS have been employed in applied archaeological remote sensing across the world with primary clusters of activity in Europe and North America (Figure 3). A secondary cluster was observed around the tropical countries of Central and South America where dense canopy is obscuring the rich archaeological landscapes and, therefore, increasingly more studies are taking place to reveal and to visualize the hidden historical terrains. Italy leads the ranking of countries where UAS-based archaeological studies have taken place (12), followed by Spain (7), the United States of America (7), Czech Republic (6), the United Kingdom (6), and Greece (4).
Drones 2020, 4, x FOR PEER REVIEW 6 of 31 Study locations were mapped from the 68 publications, and it was found that UAS have been employed in applied archaeological remote sensing across the world with primary clusters of activity in Europe and North America (Figure 3). A secondary cluster was observed around the tropical countries of Central and South America where dense canopy is obscuring the rich archaeological landscapes and, therefore, increasingly more studies are taking place to reveal and to visualize the hidden historical terrains. Italy leads the ranking of countries where UAS-based archaeological studies have taken place (12), followed by Spain (7), the United States of America (7), Czech Republic (6), the United Kingdom (6), and Greece (4).

Platforms
The majority of studies (60) reported using multirotor-style platforms, which can be attributed to easier maneuverability, higher stability, and the capacity to carry heavier payloads with smaller platform size-to-payload ratios. Only 27 studies reported the use of fixed-wing platforms. In total, 91 platforms were involved in the studies, of which 27 were fixed-wing, 42 quadcopters, 12 hexacopters, and 14 octocopters (circle-shaped, v-shaped, and X8 rotor configurations). The most commonly used platform (Figure 4) was the senseFly eBee, a fixed-wing platform which can be purchased around $25,000 USD with real-time kinematic (RTK) positioning enabled and a SODA 20 MP RGB camera, has a maximum flight time of 50 m, 33 km flight range and optional multi-spectral and thermal sensors available. Other popular choices included the DJI Phantoms and the (discontinued) 3DR SOLO. A few studies reported the use of a custom made platform.

Platforms
The majority of studies (60) reported using multirotor-style platforms, which can be attributed to easier maneuverability, higher stability, and the capacity to carry heavier payloads with smaller platform size-to-payload ratios. Only 27 studies reported the use of fixed-wing platforms. In total, 91 platforms were involved in the studies, of which 27 were fixed-wing, 42 quadcopters, 12 hexacopters, and 14 octocopters (circle-shaped, v-shaped, and X8 rotor configurations). The most commonly used platform (Figure 4) was the senseFly eBee, a fixed-wing platform which can be purchased around $25,000 USD with real-time kinematic (RTK) positioning enabled and a SODA 20 MP RGB camera, has a maximum flight time of 50 m, 33 km flight range and optional multi-spectral and thermal sensors available. Other popular choices included the DJI Phantoms and the (discontinued) 3DR SOLO. A few studies reported the use of a custom made platform.

Navigation
A high percentage of the UAS (at least 70%) used for the analyzed studies, and specifically those manufactured by DJI, Microdrones, and senseFly, were ready-to-fly and, therefore, pre-equipped with an autopilot system for navigation control. Of the studies using not ready-to-fly UAS systems, only a few reported the utilized model of autopilot, with Ardupilot APM, Pixhawk, DJI Naza, and the MikroKopter Flight-Ctrl series appearing more often. Similarly, regarding on-platform hybrid measurement units/hybrid navigation system (HMU/HNS), most platforms were already pre-equipped, and only 2 of the studies utilizing non-pre-integrated navigation units [110,112] reported which models were involved; namely an Applanix APX-15 and a NovAtel SPAN-IGM-S1. Most studies (55) reported the use of flight planning software, and only 5 reported performing manual flights, while for the majority of the rest of the studies-that did not report about this matter-it was assumed that flight planning software was used, according to the description of the acquired datasets. The MikroKopter Tool, Pix4Dcapture app, senseFly eMotion, and the free, open-source ArduPilot Mission Planner were the ones encountered more often.

Sensors
Significantly, 56 studies reported the utilization of multiple sensors, and 12 studies also reported employing multiple platforms ( Figure 5). Ten studies used LiDAR sensors, 22 studies used near-infrared (NIR) cameras, 31 studies used thermal-infrared (TIR) cameras, 26 used multi-spectral cameras (MS), and a single study used a hyperspectral (HS) camera ( Figure 6). Studies overwhelmingly used pre-equipped or off-the-shelf red, green and blue (RGB) cameras (77%), mainly in combination with other sensors. Canon cameras were a popular choice (used for 31% of the total studies), as 17 studies used Canon off-the-self cameras for RGB acquisition, while 16 studies used Canon cameras for NIR acquisition after they were subjected to a modification in order to be sensitive only in a portion of the NIR spectrum.
All reported NIR data acquisitions were performed with commercial digital cameras (digital-single-reflex, compact, and action cameras), modified by either the manufacturer of the UAS platform or the researchers. Canon PowerShot ELPH 110HS, PowerShot ELPH 300HS, and PowerShot ELPH S110 were the most frequently used cameras modified for NIR imaging (

Navigation
A high percentage of the UAS (at least 70%) used for the analyzed studies, and specifically those manufactured by DJI, Microdrones, and senseFly, were ready-to-fly and, therefore, pre-equipped with an autopilot system for navigation control. Of the studies using not ready-to-fly UAS systems, only a few reported the utilized model of autopilot, with Ardupilot APM, Pixhawk, DJI Naza, and the MikroKopter Flight-Ctrl series appearing more often. Similarly, regarding on-platform hybrid measurement units/hybrid navigation system (HMU/HNS), most platforms were already pre-equipped, and only 2 of the studies utilizing non-pre-integrated navigation units [110,112] reported which models were involved; namely an Applanix APX-15 and a NovAtel SPAN-IGM-S1. Most studies (55) reported the use of flight planning software, and only 5 reported performing manual flights, while for the majority of the rest of the studies-that did not report about this matter-it was assumed that flight planning software was used, according to the description of the acquired datasets. The MikroKopter Tool, Pix4Dcapture app, senseFly eMotion, and the free, open-source ArduPilot Mission Planner were the ones encountered more often.

Sensors
Significantly, 56 studies reported the utilization of multiple sensors, and 12 studies also reported employing multiple platforms ( Figure 5). Ten studies used LiDAR sensors, 22 studies used near-infrared (NIR) cameras, 31 studies used thermal-infrared (TIR) cameras, 26 used multi-spectral cameras (MS), and a single study used a hyperspectral (HS) camera ( Figure 6). Studies overwhelmingly used pre-equipped or off-the-shelf red, green and blue (RGB) cameras (77%), mainly in combination with other sensors. Canon cameras were a popular choice (used for 31% of the total studies), as 17 studies used Canon off-the-self cameras for RGB acquisition, while 16 studies used Canon cameras for NIR acquisition after they were subjected to a modification in order to be sensitive only in a portion of the NIR spectrum. Canon cameras were used in 9 studies, the AIRINOV Multispec4C was used in 5 studies, and the Tetracam ADC Mini-MCA and the MAPIR Survey2 cameras were each reportedly used once ( Figure  7-right). Regarding TIR sensors, 16 FLIR-manufactured cameras purposed for UAS integration were reported, but the senseFly thermoMAP was the most popular thermo-camera solution ( Figure 8). Most studies captured imagery with nadir-or near nadir-facing cameras; only a handful of studies stated they captured oblique imagery.  Canon cameras were used in 9 studies, the AIRINOV Multispec4C was used in 5 studies, and the Tetracam ADC Mini-MCA and the MAPIR Survey2 cameras were each reportedly used once ( Figure  7-right). Regarding TIR sensors, 16 FLIR-manufactured cameras purposed for UAS integration were reported, but the senseFly thermoMAP was the most popular thermo-camera solution ( Figure 8).
Most studies captured imagery with nadir-or near nadir-facing cameras; only a handful of studies stated they captured oblique imagery.  All reported NIR data acquisitions were performed with commercial digital cameras (digital-single-reflex, compact, and action cameras), modified by either the manufacturer of the UAS platform or the researchers. Canon PowerShot ELPH 110HS, PowerShot ELPH 300HS, and PowerShot ELPH S110 were the most frequently used cameras modified for NIR imaging (Figure 7-left). The most recurrently used MS camera was the Parrot Sequoia (11 studies), whereas modified Canon cameras were used in 9 studies, the AIRINOV Multispec4C was used in 5 studies, and the Tetracam ADC Mini-MCA and the MAPIR Survey2 cameras were each reportedly used once (Figure 7-right). Regarding TIR sensors, 16 FLIR-manufactured cameras purposed for UAS integration were reported, but the senseFly thermoMAP was the most popular thermo-camera solution ( Figure 8). Most studies captured imagery with nadir-or near nadir-facing cameras; only a handful of studies stated they captured oblique imagery.  Amongst the miniaturized LiDAR solutions, the Riegl VUX-1UAV and Velodyne VLP-16 were the most used, each having been utilized three times. Furthermore, the Riegl miniVUX-1UAV, the Sparkfunk LiDAR-Lite V2, and the Yellowscan Mapper were used once, while there were also two unreported LiDAR sensors involved in the recorded studies.

Data Products and Applications
A major part of the recorded UAS-based archaeological prospection activities involved the interpretation of visible spectrum orthophotos/orthophoto-mosaics (68%) and DSMs (55%; for relative percentages see Figure 9), which were mainly produced using structure-from-motion (SfM) and multiple-view-stereo (MVS) approaches. The level of involvement of these products in archaeology can be explained by the high level of automation achieved in the last years, which allows almost automated mapping workflows-from acquisition to analysis of mapping results-for archaeological applications. Flight planning, pre-processing of the images, image-based modeling, production of digital elevation models (DEMs), production of visible, thermal and index maps, and point cloud and image classifications can be realized using the same workstation and software, with minimal interventions, even in real-time. Studies overwhelmingly utilized the Pix4D software platform as an integrated solution to capture imagery datasets (through the mobile application) and create digital models or orthoimages for classification and analysis. Agisoft PhotoScan/Metashape and ArcGIS have also been implemented in a handful of studies for the digitization and analysis  Amongst the miniaturized LiDAR solutions, the Riegl VUX-1UAV and Velodyne VLP-16 were the most used, each having been utilized three times. Furthermore, the Riegl miniVUX-1UAV, the Sparkfunk LiDAR-Lite V2, and the Yellowscan Mapper were used once, while there were also two unreported LiDAR sensors involved in the recorded studies.

Data Products and Applications
A major part of the recorded UAS-based archaeological prospection activities involved the interpretation of visible spectrum orthophotos/orthophoto-mosaics (68%) and DSMs (55%; for relative percentages see Figure 9), which were mainly produced using structure-from-motion (SfM) and multiple-view-stereo (MVS) approaches. The level of involvement of these products in archaeology can be explained by the high level of automation achieved in the last years, which allows almost automated mapping workflows-from acquisition to analysis of mapping results-for archaeological applications. Flight planning, pre-processing of the images, image-based modeling, production of digital elevation models (DEMs), production of visible, thermal and index maps, and point cloud and image classifications can be realized using the same workstation and software, with minimal interventions, even in real-time. Studies overwhelmingly utilized the Pix4D software platform as an integrated solution to capture imagery datasets (through the mobile application) and create digital models or orthoimages for classification and analysis. Agisoft PhotoScan/Metashape and ArcGIS have also been implemented in a handful of studies for the digitization and analysis Amongst the miniaturized LiDAR solutions, the Riegl VUX-1UAV and Velodyne VLP-16 were the most used, each having been utilized three times. Furthermore, the Riegl miniVUX-1UAV, the Sparkfunk LiDAR-Lite V2, and the Yellowscan Mapper were used once, while there were also two unreported LiDAR sensors involved in the recorded studies.

Data Products and Applications
A major part of the recorded UAS-based archaeological prospection activities involved the interpretation of visible spectrum orthophotos/orthophoto-mosaics (68%) and DSMs (55%; for relative percentages see Figure 9), which were mainly produced using structure-from-motion (SfM) and multiple-view-stereo (MVS) approaches. The level of involvement of these products in archaeology can be explained by the high level of automation achieved in the last years, which allows almost automated mapping workflows-from acquisition to analysis of mapping results-for archaeological applications. Flight planning, pre-processing of the images, image-based modeling, production of digital elevation models (DEMs), production of visible, thermal and index maps, and point cloud and image classifications can be realized using the same workstation and software, with minimal interventions, even in real-time. Studies overwhelmingly utilized the Pix4D software platform as an integrated solution to capture imagery datasets (through the mobile application) and create digital models or orthoimages for classification and analysis. Agisoft PhotoScan/Metashape and ArcGIS have also been implemented in a handful of studies for the digitization and analysis procedures, In UAS-based archaeogeography applications, the rectified images, ortho-mosaics and raster elevation images have been used in a great extent, and analyzed through classification and shape analysis techniques, to interpret the shape of historical structures [110], the distribution of historical [69] and traditional structures [78], buried remains [77,85], and archaeological artifacts [74,114,116].
Lastly, five applications relevant to UAS-based site monitoring and landscape archaeology have been recorded, most reporting the use of MS data acquisitions. Fenger-Nielsen et al. [64] used NIR intensities detected with a Parrot Sequoia, in combination with soil-content data from site-sampling and satellite imagery, to study the distinct spectral characteristics of vegetation within archaeological sites in Greenland, which showed great potential for archaeological investigations in the Arctic. Khan et al. [73] exploited terrain data obtained with the survey-grade VUX1-UAV LiDAR and two MS cameras, and integrated them with in situ collected archaeological, archaeobotanical, paleo-ecological, and soil data to investigate the scale and nature of the impact of pre-Columbian humans in transforming the Amazonian rainforest landscapes. Mather et al. [82] performed classifications on RGB and NIR imagery data captured with different UAS, and then overlaid-also UAS-captured-topographic data in order to understand the origin of relict landform features. Sonnemann et al. [113] overlaid orthomosaics produced with UAS imagery on high-resolution LiDAR-derived DEM to investigate the topography of pre-colonial settlements in the Caribbean.

State-of-the-Art
Overall, the significant developments in unmanned navigation, drone platform manufacturing, False-color and TIR-orthoimages appeared often in prospections studies (37%, and 47% of the prospection studies respectively). False-color multichannel composites were produced either by using the MS datasets for image-based modeling directly or by combining overlapping orthoimages of different wavelengths. Most MS composites where either reduced through PCA [50,84,109] or processed for normalized difference vegetation index (NDVI) mapping [50,52,54,56,57,60,[66][67][68][70][71][72]75,76,[84][85][86]97,99,104,109], in order to enhance the contrast between the proxies of buried remains and the landscape matrix. It is worth mentioning that a few studies [50,60,75,86,97,99] reported the use of multiple radiometric vegetation and soil indices to identify which wavelength combinations maximized the local contrasts. Another notable finding was that all thermal investigations were purposed towards archaeological prospection activities. Multi-temporal thermal acquisitions often appeared in the analyzed studies. At the same time, McLeester et al. [84] also pointed out how thermal orthoimages have to be corrected through several steps-from stripping caused by the TIR camera's periodic self-calibration and the thermal sensor's temperature variations during acquisition-to accurately extract temperature information. Lastly, few prospection studies also reported the use of DTMs to identify topographic reliefs caused by buried historical remains [58,59,73,93,96,98,112] Activities referring to the accurate visualization of archaeologically rich terrain with UAS-assisted approaches have either explored the use of DSMs or DTMs. While DSMs have been mainly produced with SfM/MVS image-based approaches, DTMs of vegetated areas have been exclusively produced with miniaturized LiDAR sensors [87,96,98,110] or by filtering 3D point clouds derived from image-based modeling with NIR imagery datasets, before constructing the terrain model [49,106]. To a lesser extent, the visualization of historical terrains towards the better interpretation of historical landscapes has been explored in the bibliography by interpretative mapping techniques [65,88,96,100].
In UAS-based archaeogeography applications, the rectified images, ortho-mosaics and raster elevation images have been used in a great extent, and analyzed through classification and shape analysis techniques, to interpret the shape of historical structures [110], the distribution of historical [69] and traditional structures [78], buried remains [77,85], and archaeological artifacts [74,114,116].
Lastly, five applications relevant to UAS-based site monitoring and landscape archaeology have been recorded, most reporting the use of MS data acquisitions. Fenger-Nielsen et al. [64] used NIR intensities detected with a Parrot Sequoia, in combination with soil-content data from site-sampling and satellite imagery, to study the distinct spectral characteristics of vegetation within archaeological sites in Greenland, which showed great potential for archaeological investigations in the Arctic. Khan et al. [73] exploited terrain data obtained with the survey-grade VUX1-UAV LiDAR and two MS cameras, and integrated them with in situ collected archaeological, archaeobotanical, paleo-ecological, and soil data to investigate the scale and nature of the impact of pre-Columbian humans in transforming the Amazonian rainforest landscapes. Mather et al. [82] performed classifications on RGB and NIR imagery data captured with different UAS, and then overlaid-also UAS-captured-topographic data in order to understand the origin of relict landform features. Sonnemann et al. [113] overlaid orthomosaics produced with UAS imagery on high-resolution LiDAR-derived DEM to investigate the topography of pre-colonial settlements in the Caribbean.

State-of-the-Art
Overall, the significant developments in unmanned navigation, drone platform manufacturing, integrated sensor miniaturization, and mapping software that have taken place over the last decade, established UAS-based approaches as a significant part of photogrammetry and remote sensing [117]. Benefiting from the above, archaeological science has taken one step forward into embracing these new technologies, through various applications-as discussed earlier. Archaeological low-altitude sensing has substantially evolved from the use of platforms purposed for recreational and photographic uses. It has not only engulfed the advancements of those sensors and integrated systems specifically oriented towards archaeological applications but has also benefited from the progress in other fields, for example, precision agriculture and monitoring of structures, who profit from similar UAS-borne sensing. Recent archaeological studies expand the scientific horizons beyond the visual interpretation of the data products and discuss how previously invisible and undocumented features are revealed, while simultaneously paying attention to the spatial and radiometric accuracy and precision of presented results. It goes without saying that high-resolution and high-accuracy products, and information retrieval from multiple wavelengths, are accompanied by considerable costs. Although a trend is evident regarding researchers who invest in high-end fully autonomous integrated UAS, more application-oriented projects seem to prefer custom solutions. Therefore, in this section, we provide information on both integrated ready-to-fly UAS and specialized options for payload integration. Table 1 provides some examples of integrated solutions, which are purposed or can be repurposed for archaeological remote sensing surveys (also examples in Figure 10). The reported integrated systems are ready-to-fly UAS with pre-equipped navigation system, autopilot, sensors, gimbals, and all necessary electrical on-board equipment.

Platforms
UAS operated for archaeological remote sensing mainly fall into the Micro (<2 kg weight, up to 200 m altitude, <5 km radius, <1 h endurance), Mini (2-20 kg weight, up to 3000 m altitude, <25 km radius, 1-2 h endurance), and seldom Small (20-150 kg weight, up to 5000 m altitude, <50 km radius, 1-5 h endurance) categories (after Qi et al. [118]). While the quality of acquired datasets depends largely on on-board sensors (described in Sections 5.4-5.7), platform typology and configuration play an essential role in the success of the remote-sensing mission, simultaneously constraining the payload that may be deployed and the flight planning. Fixed-wing UAS have a clear advantage in archaeological surveys because their longer flight autonomy allows the coverage of much more extensive historical sites and landscapes than the average multi-rotor (Table 2). Fixed-wing aircrafts' increased stability allows greater control over flight parameters and the quality of collected data. However, the competition with rotary-wing aircraft is always present because multi-rotors have greater maneuverability and allow heavier payloads and more customizability, therefore having

Platforms
UAS operated for archaeological remote sensing mainly fall into the Micro (<2 kg weight, up to 200 m altitude, <5 km radius, <1 h endurance), Mini (2-20 kg weight, up to 3000 m altitude, <25 km radius, 1-2 h endurance), and seldom Small (20-150 kg weight, up to 5000 m altitude, <50 km radius, 1-5 h endurance) categories (after Qi et al. [118]). While the quality of acquired datasets depends largely on on-board sensors (described in Sections 5.4-5.7), platform typology and configuration play an essential role in the success of the remote-sensing mission, simultaneously constraining the payload that may be deployed and the flight planning. Fixed-wing UAS have a clear advantage in archaeological surveys because their longer flight autonomy allows the coverage of much more extensive historical sites and landscapes than the average multi-rotor (Table 2). Fixed-wing aircrafts' increased stability allows greater control over flight parameters and the quality of collected data. However, the competition with rotary-wing aircraft is always present because multi-rotors have greater maneuverability and allow heavier payloads and more customizability, therefore having more options for sensor integration. Table 2. Comparison between different features of fixed-wing and multi-rotor UAS, modified from Jeziorska [119].

Fixed-Wing
Multi-Rotor Advantages long flight autonomy greater maneuverability better control of flight parameters more compact and portable higher control of data quality easy to use greater stability higher payload capacity higher flight safety more flexibility in payload configuration ability to hover small landing/take-off zone Disadvantages less compact and portable shorter range challenging to fly less stable in the wind larger take-off/landing site needed Multi-rotor UAS are additionally more compact (speaking for the same platform body size-to-payload weight ratio), and subsequently more easily transportable. Despite their short flying duration, which limits the archaeological area that can be covered within a single flight, they have some distinct attributes that may be necessary in certain contexts, as the ability to hover and capture data while remaining over one place, the ease of capturing oblique imagery, and vertical take-off and landing that allows for more flexible deployment in areas that would be inaccessible with fixed-wing aircraft. Some conventional multi-rotor frames used in customized UAS for archaeological applications are MikroKopter's MK8-2500 (8 rotors, folded dimensions 64 cm × 60 cm, max. payload 2.5 kg) and MK8-3500 (8 rotors, max. payload 3.5 kg), and VulcanUAV's Black Widow (4 rotors, max payload 4.6 kg) and Raven (8 rotors-X8 configuration, max. payload 10 kg).

Orientation Systems
The miniaturization of computer boards, Global Navigation Satellite Systems (GNSS) receivers and antennas, inertial measurement units (IMUs) and, in general, electronics has allowed the integration of hybrid measurement units (HMU) for UAS whose measurements can be processed, in a hybrid navigation system (HNS) or in post-processing, in a hybrid orientation system (HOS). The results depend on the quality of the GNSS receiver and the GNSS antenna, and the accuracy of the attitude part of the orientation is highly dependent on the IMU quality and flight dynamics. To deliver orientation parameters, and data products at cm-level with increased reliability, primarily two modifications of kinematic GNSS measurements are being adopted for UAS applications. RTK-which considers that there is real time communication of the UAS with a ground reference station (using radio link)-delivers corrections to GNSS measurements during the flight. Post-processed kinematic (PPK), on the other hand, depends on corrections from a reference station that are applied post-flight [120,121]. As RTK-enabled receivers have already been available on several commercial platforms, and PPK is becoming more common for UAS-based archaeological surveys, it is useful to review the current capabilities of HMU and HNS solutions for unmanned aircraft operations. Therefore, the state-of-the-art integrated solutions are presented in Table 3, taking into consideration that the values given are simply indicative as they include general specifications and optimal testing conditions. Note: Φ: phase measurements; ρ: code measurements; σ p hz : horizontal position accuracy (RMS); SPS: standard positioning service; RTK: real-time kinematic; RTK*: real-time kinematic post-processed; σ α : linear accelerations' noise (PSD level); σ ω : angular rates' noise (PSD level); σ θ,γ : roll and pitch precision (whole spectrum); σ ψ : heading precision (whole spectrum); NA: not available.

Light Detection and Ranging (LiDAR) Sensors
The reduction in LiDAR sensor size and price are making them more common for UAS-based archaeological surveys. The market of 3D laser scanners for unmanned platforms has grown rapidly, and the technological developments are increasing the quality of data acquired by these sensors. This creates the prospect of replacing airborne LiDAR since essential characteristics of LiDAR data are largely unaffected by the carrying platform, which implies that existing well-developed processing techniques can be used on these data. Limitations caused by the tradeoff between performance and size or cost of LiDAR, can be partially overcome by the proximity of the sensor and the surveyed area in comparison to airborne scanning. Presently Quanergy, Riegl, and Velodyne dominate the market of LiDAR sensors manufactured to be mounted on UAS (examples in Figure 11), as the overwhelming majority of integrated payload for scanning, include their products.

Near-Infrared and Multi-Spectral Cameras
The exploitation of NIR imagery can contribute significantly to UAS-based archaeology [122], and therefore various sensor solutions have been explored to incorporate the NIR spectrum in prospection-related applications. As the meta-analysis revealed, the Canon S110 digital cameras, modified for red-edge and near-infrared imaging, have been frequently used over the past decade, serving as a default multi-spectral solution for the senseFly fixed-wing aircrafts until 2014. These cameras are currently being replaced by various high-resolution models, as the modification of compact and digital single-lens reflex (DSLR) cameras, for beyond-visible acquisition, becomes more

Near-Infrared and Multi-Spectral Cameras
The exploitation of NIR imagery can contribute significantly to UAS-based archaeology [122], and therefore various sensor solutions have been explored to incorporate the NIR spectrum in prospection-related applications. As the meta-analysis revealed, the Canon S110 digital cameras, modified for red-edge and near-infrared imaging, have been frequently used over the past decade, serving as a default multi-spectral solution for the senseFly fixed-wing aircrafts until 2014. These cameras are currently being replaced by various high-resolution models, as the modification of compact and digital single-lens reflex (DSLR) cameras, for beyond-visible acquisition, becomes more prevalent. At the same time, various lower-resolution lightweight camera options for UAS-based MS imaging are available, having the advantage of more than three narrower bands. Table 5 summarizes the characteristics of some typical and/or representative camera options (in Figure 12).

Hyperspectral Cameras
To cover the need for the detection of information from multiple very narrow bands of the electromagnetic spectrum [123], and towards the more accurate calculation of vegetation and soil indices, HS imaging sensors have consistently been miniaturized, and can currently be mounted on UAS platforms. Some of them are listed in Table 6 (also examples in Figure 13). It is the authors' opinion that these sensors will continue to play a significant role in UAS-based archaeological prospection, and geoarchaeology.

Hyperspectral Cameras
To cover the need for the detection of information from multiple very narrow bands of the electromagnetic spectrum [123], and towards the more accurate calculation of vegetation and soil indices, HS imaging sensors have consistently been miniaturized, and can currently be mounted on UAS platforms. Some of them are listed in Table 6 (also examples in Figure 13). It is the authors' opinion that these sensors will continue to play a significant role in UAS-based archaeological prospection, and geoarchaeology.

Thermal Cameras
There have been essential advancements in thermal camera miniaturization in recent years. Lightweight, small-size LWIR imagers, such as those developed by FLIR, were first introduced in a military context for remote reconnaissance and are becoming more common in UAS-based remote-sensing applications such as archaeological prospection. Table 7 compiles some existing products in the family of thermal sensors, suitable for light UAS (also examples in Figure 14).

Thermal Cameras
There have been essential advancements in thermal camera miniaturization in recent years. Lightweight, small-size LWIR imagers, such as those developed by FLIR, were first introduced in a military context for remote reconnaissance and are becoming more common in UAS-based remote-sensing applications such as archaeological prospection. Table 7 compiles some existing products in the family of thermal sensors, suitable for light UAS (also examples in Figure 14). There have been essential advancements in thermal camera miniaturization in recent years. Lightweight, small-size LWIR imagers, such as those developed by FLIR, were first introduced in a military context for remote reconnaissance and are becoming more common in UAS-based remote-sensing applications such as archaeological prospection. Table 7 compiles some existing products in the family of thermal sensors, suitable for light UAS (also examples in Figure 14).

Ground-Penetrating Radars
A ground-penetrating radar (GPR) is an active non-destructive geophysical sensing technique that utilizes electromagnetic radiation in the microwave band and has always been entangled with archaeological prospection for the subsurface mapping of artifacts, features, and patterning [124,125]. Although there are currently no integrated payload solutions for UAS-borne microwave-based detection, a few recent studies report experiments towards the manufacture of customized systems for GPR non-destructive applications [126][127][128].

Conclusions
The last decade was marked by a radical miniaturization and integration of UAS-mounted sensors, which gradually fostered the adoption of low-altitude sensing techniques for archaeological applications, including but not limited to prospection. Notwithstanding the considerable number of works reviewed here, UAS-based archaeological remote-sensing applications and, especially, those dealing with the beyond-visible spectra to identify multi-spectral contrast variations, are still scarce. This is most likely because the relevant technology has only recently reached a certain level of maturity and high-resolution solutions remain considerably expensive. The recently observed trends, regarding increasingly more metrically and radiometrically accurate data-acquisition and data production in archaeological surveys, and the adoption of well-established processing and analytical techniques from satellite and airborne sensing, suggest a promising perspective. However, the aspects of spatial precision and accuracy still remain undocumented in numerous archaeological surveys which suggests a need for better training regarding metric concepts and for increasing the collaborations between archaeologists and geomatics experts to achieve optimal results in archaeological remote-sensing projects. Metric, radiometric and semantic contents of acquired archaeological data and meta-data should not be neglected as they contain valuable information for archaeological interpretations. It should be further highlighted that automation in the detection of historical residues remains an undeniably complex and challenging task due to the unique morphological, stratigraphical, topographical and archaeological characteristics of each archaeological site [129][130][131]. For this reason the majority of UAS-borne prospection studies still depend on the parallel acquisition of data with ground-based geophysical methods such as electrical resistivity surveys, ground-penetrating radar, electromagnetic conductivity surveys, and magnetic gradiometry surveys, on historical aerial footage, and on satellite datasets, which complete our perspectives over historical terrains. Lastly, the authors would like to point out that, despite the observed allocation of the analyzed studies on a global scale, the gaps on the relevant map do not necessarily reflect the contemporary worldwide distribution of archaeological remote-sensing research. There are various reported examples of innovative archaeological studies in Oceania and Asia [132][133][134][135]-actively using drones-which due to the strictly set selection criteria were excluded from the presented meta-analysis. The typology of the historical remains also plays a large part in this distribution anomaly. However, UAS-based remote sensing is widely applied in these areas, mainly directed towards heritage recording.

Conflicts of Interest:
The authors declare no potential conflict of interest.