# Autonomous Service Drones for Multimodal Detection and Monitoring of Archaeological Sites
## 5 Conclusions and Future Work  
In this paper, we propose a drone-based framework for multimodal detection and modeling of built cultural heritage. We control the service drone using a GCS to inspect an archaeological landscape and monitor architectural cultural heritage sites. We mount RGB, depth, and thermal sensors on the drone for low-altitude data acquisition. After the completion of an inspection flight, recorded videos are uploaded to a cloud server for further processing and analysis. We use a proposed two-stage multimodal depth-to-RGB and thermal-to-RGB mosaicking algorithm to generate orthomosaics of the monitored area. The generated mosaics are passed to our detection algorithm to detect cultural heritage sites within the inspected area with an accuracy of 90% and a root mean squared error of 0.4. End-users receive detailed reports after each inspection flight, including 3D models of the detected cultural heritage sites. The proposed user interface accumulates the reports of detected sites for temporal tracking. We tested our proposed framework in an indoor lab environment where we built a replica of a large-scale archaeological site, and concluded it efficiently detects and documents cultural heritage structures and facilitates the process of preservation. We compared our framework with a workflow we developed for the detection of built cultural heritage using state-of-the-art commercial photogrammetry software. The results showed that we managed to cost-effectively replicate, to some extent, the mosaicking and alignment functionalities of commercial photogrammetry software while adding a new depth-based modality and increasing flexibility. We also proved that our proposed framework can be used to identify the best time in which thermal anomalies in the ground are chromatically more evident on the images. Our framework's main limitation is the accumulation of transformation estimations in the mosaicking algorithm, which leads to deterioration in the performance with time. We have addressed this limitation by deploying the AI-based detection in the subsequent steps of the proposed framework. Further research on mosaicking techniques is needed to address this limitation better.  
Conceptualization, A.K., G.C., T.B. and M.G.; data curation, A.K., G.C., T.B. and M.G.; formal analysis, A.K., G.C., T.B. and M.G; funding acquisition, A.K., G.C., T.B. and M.G.; investigation, A.K., G.C., T.B. and M.G.; methodology, A.K., G.C., T.B. and M.G.; project administration, A.K., G.C., M.A., T.B. and M.G.; resources, A.K., G.C., T.B. and M.G.; software, A.K., G.C., T.B. and M.G.; supervision, A.K., G.C., M.A., T.B. and M.G.; validation, A.K., G.C., T.B. and M.G.; visualization, A.K., G.C., T.B. and M.G.; writing--original draft, A.K., G.C., M.A., T.B. and M.G.; writing--review and editing, A.K., G.C., M.A., T.B. and M.G.. All authors have read and agreed to the published version of the manuscript.  
The research was funded by Abu Dhabi University, Faculty Research Incentive Grant (19300483--[PERSON]), United Arab Emirates. Link to Sponsor website: [[https://www.adu.ac.ae/research/research-at-adu/overview](https://www.adu.ac.ae/research/research-at-adu/overview)]([https://www.adu.ac.ae/research/research-at-adu/overview](https://www.adu.ac.ae/research/research-at-adu/overview)) (accessed on 30 October 2021).  
Not available.  
The authors declare no conflict of interest.