## 3 Results  
Using the proposed methodology, we succeeded in detecting fortified sites that were previously unknown in Blad Talh. The automated detection of fortified sites exhibited high accuracy, equal to that of human detection. The results show that machine learning performed with high accuracy (see Table 2). To the best of our understanding, as of mid-2021, the P1B image received on 16 July 2021 was the most current VHR optical satellite image covering Blad Talh that was accessible online through the GEE. The GEE and machine learning have proven to be applicable instruments for automatically detecting archaeological sites in satellite images. The GEE, machine learning, and remote sensing were highly effective at the site scale and identified pixels of archaeological remains. Furthermore, pixels of fortified structures were successfully detected in Blad Talh, confirming the ability of the GEE to quickly run an analysis on a substantial scale. The discovery and distribution of archaeological sites extended towards the southern part of the Blad Talh area and the surrounding pre-desert towards the east, which was a region that was relatively unreachable in past investigations. Predictably, the allocation of the fortified sites detected by the algorithm was concentrated in the Blad Talh area. Sentinel-1 texture differences between layers were accentuated in the area (see Figure 3). The algorithm's ability to detect fortified archaeological sites is likely due to polarisation bands and the capacity of the SAR C-band to penetrate soil. Because of vegetation and sand cover, the RF potential produced only some well-illustrated square shapes. The majority of the newly proposed fortified sites presented segmented square shapes or amorphous groups of pixels. It is quite possible that in addition to these five detection sites, there are also remains of sand-covered archaeological sites with low RF probability values. Notably, when high-resolution visible images were used, the algorithm still assisted in identifying small pixels or backscattering. SAR alone showed no significant change in land cover. As a result, SAR alone cannot give enough information to identify archaeological sites. Also, the Pleiades images are not capable of segregating the spectral structures of the sites on their own using an RF classifier. Furthermore, our algorithm was designed to integrate Sentinel-1 and PB1 images for better archaeological discovery, an approach that showed accurate results (see Figure 4). Comparing the results between high-resolution imagery PB1 without SAR images and the virtual absence of sites, their combination for archaeological detection of the known fortified site was used as a validation set (see Figure 4). When possible, the new detection was matched to previous research on past archaeological sites [8; 47; 67]. The \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Overall Accuracy** & **Kappa Coefficient** \\ \hline Validation & 0.93 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results of the study area. Figure 3: Feature importance in the classification model. archaeological sites relative to the existence of sand dunes suggested that many more sites can be covered or located underground in the pre-desert, and thus, the aeolian sediments in southern Tunisia may have played an important role in both the field records of the area and the discernibility of the archaeological ruins. Regions with a large number of sites were located in open mudflats that were distributed throughout the area, while many sites were still relatively covered underground. Five newly detected fortified sites were identified (see Figure 5). The distribution of the archaeological sites extended towards the southern part of the Blad Talh area and the surrounding pre-desert towards the east, which was a region that was relatively unreachable in past investigations. The resulting cluster of high-RF probability pixels showing fortified sites was used to rebuild the regions.