# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 2 Literature Review
These cameras can now be mounted under drones to generate high-resolution thermal images, with a GSD of two centimeters flying at 40 m of altitude, while significantly reducing the cost of the missions carried out. The use of programmable drones guarantees performing flights in a precise time frame. The timing of an aerial thermal survey is one of the most crucial factors in determining its success, as imagery acquired just an hour apart typically shows archaeological elements in different ways [26; 27]. Due to the numerous factors that might influence the thermal characteristics of certain features, identifying the best time for data collection, whether seasonally or during a given diurnal cycle, remains difficult and is considered to be one of the limitations of thermography. To date, the best examples in this field come from the aforementioned work carried out by the authors in [27; 28]. In particular, the research carried out at the Blue J site in New Mexico made it possible to highlight the importance of making multiple flights during the same day to identify the best time slot for recording based on the variation in the daily thermal cycle. In this case, the researchers performed four flights with thermal camera between 20 and 21 June 2013 (9:58 p.m., 5:18 a.m., 6:18 a.m., 7:18 a.m.) and a flight with standard camera in RGB (5:45 a.m.). The results showed that only in the flight performed before dawn, at 5:18 a.m., the greatest number of character archaeological anomalies were evident. This was probably because stones of the collapsed walls and masonry structures absorb heat longer than the surrounding desert soil, making the stones more visible in the colder hours of the morning than at the beginning of the night when the ground still holds heat from daily solar radiation [27]. The work carried out by the same team at the Enfield Shaker Village site in New Hampshire also highlighted the importance of carrying out a multitemporal thermal recognition to have a complete thermographic analysis of the survey area. In this case, the flights were carried out at different times of the same day, but also in different months of the year (October 2016, May and June 2017, October 2017). The results obtained showed, for this area, the maximum  
Figure 4: Use of a manual thermal camera for the analysis of different concentration of artifacts scattered on the ground as introduced by the authors in [22]. visibility of the archaeological anomalies in the time span ranges from immediately after sunset to a few hours later, while the contrast (albeit still visible) decreased in flights carried out before dawn, only to disappear almost completely after sunrise [28]. Airborne and satellite photography remote sensing has become an important part of archaeological study and cultural resource management. Remote sensing, when combined with field research, allows for quick and high-resolution documentation of old sites [29; 30; 31]. It also makes it possible to document and monitor old sites that are unreachable to field-work, destroyed, or threatened [32; 33]. Archaeologists have been experimenting with computational approaches in remote sensing-based archaeological prospection since the mid-2000s [34; 35]. Convolutional neural networks (CNNs) are increasingly being used to solve difficult computer vision problems including face detection, self-driving automobiles, and medical diagnostics [36]. Although deep learning requires huge training datasets, transfer learning allowed pre-trained CNNs to be used in domains where training datasets were limited, such as archaeology [37; 38]. Machine-learning techniques and algorithms have gradually been included into remote sensing-based archaeological research in recent years, allowing for the automatic discovery of sites and characteristics. Most of these applications have used high-resolution datasets such as LiDAR [39] or WorldView imagery [40; 41] to detect small-scale characteristics.