# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 3 Materials and Methods  
### Drone-Based Archaeological Aerial Monitoring Challenges  
As many heritage sites are currently threatened by anthropogenic and natural destruction, heritage detection and monitoring is essential for the preservation and integrity of cultural heritage structures. Although a long history of experimental data shows that depth imaging and aerial photogrammetry and thermography are powerful tools that can reveal a wide range of both surface and subsurface archaeological features, hurdles including the excessive deployment cost, and the spatial resolution of aircraft or satellite-acquired imagery have restrained the use of these tools in cultural heritage detection. Recent advances in the sophistication of vision-based sensors, artificial intelligence algorithms, and service drones are revolutionizing archaeologists' ability to prospect for ancient cultural features across large areas at a low cost. These advances offer potentially transformative perspectives on the archaeological landscape and bridge the gap between satellite, low-altitude airborne, and terrestrial sensing of historical landscapes [27; 28].  
Due to the availability of several sensing devices with high precision and accuracy, data processing and storage capabilities using cloud computing, and affordable machine learning and big data analysis technologies, digitization of reconnaissance flights is ramping up. Digitization of flights improved the conventional practices of analyzing archaeological landscapes. It also allowed archaeologists to use real-time sensing data to detect and forecast possible changes and anomalies in often inaccessible built cultural heritage. Researchers have proposed using drone platforms with visible, infrared, multispectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied nondestructive techniques [44]. To improve the performance of mapping in archaeological applications, multiple photogrammetry software platforms were developed to capture imagery datasets and create digital models or orthoimages. These models are used to classify and analyze the mapping results [9].  
In archaeology, materials on and below the ground surface absorb, emit, transmit, and reflect thermal infrared radiation at different rates due to differences in composition, density, and moisture content. Accordingly, aerial thermographic imaging is used for the detection of a wide range of archaeological features when (1) there is a sufficient contrast in the thermal properties of archaeological features and the ground cover; (2) the archaeological materials are close enough to the surface to be affected by heat flux; and (3) the image is acquired at a time when such differences are pronounced. Multiple pieces of proprietary software have been developed allowing almost automated mapping workflows, from acquisition to analysis of mapping results, for archaeological applications. Common issues that arise when using these pieces of software include the automated alignment of visual image frames captured out of specific timeframes, the alignment of small thermal images that lack sufficient variability, and the time-consuming 3D reconstruction for spatial analysis [45].  
Aerial multimodal monitoring of cultural heritage provides a better understanding of long-term settlement patterns and historical change [46]. The early identification of changes in cultural heritage structure and the preventive conservation are critical for the preservation of cultural sites and monument. In this work, we propose a framework for the detection and monitoring of archaeological sites, and a user interface to assess and track changes and deterioration over time. Our proposed framework is intended to combine multiple imaging datasets to improve archaeological feature visibility. We compare the proposed framework to the procedure carried out using proprietary software and discuss the results.