## 5 Conclusions  
In summary, we present an integrated workflow that combines SAR images and optical Pleiades imagery with basic spatial analysis in the Google Earth Engine for automatically detecting fortified sites. The random forest-based approach demonstrates that it is a useful tool for overcoming problems with data size and structure. Our research illustrates the applicability of this detection technique to fortifications and demonstrates that it is possible to automate fortification discoveries for the first time in Tunisia. It is possible to summarise insights and knowledge directly from the data, and the algorithm is able to highlight relationships between archaeological sites. The machine-learning algorithm we use is capable of identifying all the currently known sites in the study area and the accurate locations of new fortified sites. The approach provides outputs that are significantly superior to those obtained with a single-source RS technique. RS-based applications in arid and semi-arid regions elsewhere can benefit from the integration of globally available Pleiades and Sentinel data into an accessible, flexible, and repeatable GEE environment to perform and evaluate machine learning workflows. Even though the approach shows accurate results, the limited availability of VHR data and the expense to obtain the data are considered a limitation of this study that can be improved in the future. Archaeological data, along with landscape analysis and mapping items affecting site visibility, indicates that these are only a few of the fortified sites in the region, many of which may be covered by sand in the surveyed area. We discovered fortified sites hidden by thick vegetation and confirmed these sites using field investigation, remote sensing, and machine learning. The outcome of the work presented here provides advances in the technology and archaeology domains. Technologically, using these methods, various derived models are used to evaluate their abilities in investigating the micro-topography of a site of archaeological importance under a sand dune. The results viewed from an archaeological perspective are interesting. The machine learning-based method enabled us to detect five fortified sites for which only some data was obtainable from past records and shed new light on the fortified KSAR GROUECH structure dating back to the Byzantine period (6 th century), for which only the presence of a fort was well-known. The findings are validated by on-site  
Figure 7: Comprehensive archaeological map of the Blad Talh landscape in the ancient Roman period: localities and ancient road networks. Modified from [71]. processing, demonstrating that the automatic procedure can identify and extract the major distinctive characteristics of Roman settlements, such as villas and tower walls. Notably, the exploration elaborated in this research is promising in terms of technological invention. By providing for previous quantitative examinations in this region, our exploration raises awareness of the need to use quantitative styles to address more pressing questions, similar to those about the protection and preservation of threatened archaeological sites. As a result, one of the benefits of this research is that it demonstrates, from the perspective of archaeologists, the capability of satellite data and machine learning to discover buried archaeological sites. While this study focuses on archaeological sites in southern Tunisia, the proposed approach is effective in terms of time and cost, particularly in locations where data availability is scarce. Validation of this approach was proved using the nature and characteristics of the study area, and we expect that the classification accuracy of our approach would be further improved by using a convolutional neural network (CNN) and images obtained by the Google Earth Engine. Conceptualisation: N.B., W.X. and A.E. ; methodology, A.E. and N.B. ; software, N.B. and A.E. ; validation, N.B., W.X and A.E. ; formal analysis, N.B.,W.X. and A.E. ; investigation, N.B., M.T. and A.E.