## 4 Discussion  
The RF method, which has been used in the classification of remote sensing data since the early 2000s, was chosen as the classification algorithmic rule for this study due to its simplicity of implementation, robustness, and possible predictability [63; 68; 69]. The challenge we faced was the discovery of fortified sites. Our objective was to develop a straightforward yet efficient methodology that combined remote sensing techniques and automated archaeological remains discovery, as well as to evaluate the potential offered by the combined use of remote sensing data and machine learning to discover fortified sites in arid areas. Using a substantive approach, we confirmed an advantage similar to that of other applications of machine learning techniques, and we show that the RF potential field-detected sites may be considered ancient settlements. In this research, given the high likelihood of improvement in the classification accuracy, we assessed the RF's performance in a wider context (different archaeological features or sites). As shown in the results section, the performance of the RF classifier is highly satisfactory, with a kappa coefficient of 0.91 and a precision and overall accuracy of 0.93 in the \"fortified site group.\" The RF potential area highlights five unknown locations shown on historical maps. The sites are partly hidden by sand, and the RF potential only shows different parts of the structures' fortified sites, which was verified by a field survey. The discovery of these sites is very important because it documents the existence of ancient Roman fortifications. Based on previous studies, there are more sites of Roman civilisation in this area [8; 47], and only some small sites have been uncovered by remote sensing methods. Currently, new sites could be included with those previously identified and dated by [PERSON] et al. [8; 47; 67]. To try to understand the changes in the settlement distribution over time, research in southern Tunisia documented all sites encountered, including those of the Roman era. Considering that most of the previously reported sites were settlements attributed to periods of Roman civilisation, there is a high possibility that most of the newly discovered sites were also occupied during the same period. Fortified structures such as fortresses and defensive works are fairly common features of the archaeological landscape in southern Tunisia. Such features have previously been uncovered in the city of Gafsa. In this study, we were able to locate five fortified sites situated south of the Blad Talh area. The fortified sites were built on approximately level ground in a nearly square form that is covered by sand dunes. The layout showed visible signs of erosion on all sides, and lost pits were visible, especially on the south side. Based on our findings in the literature, we propose a suitable reconstruction of the ancient settlement in Gafsa, indicating that the fortifications in the locale were a defence project planned to defend, protect, and control the Limes [8; 70; 71; 72; 73; 74]. We found fortified sites located in the same area and close to each other, in contrast to previous research [8]. The fortified sites were separated by a distance of approximately 2.5 km. From the machine learning and P1B data of the study area, a red-dashed line rectangle can be seen in Figure 5. Using machine learning and P1B data surveying, we demonstrate the detection of hidden fortified sites in Gafsa, which is well-situated across the frontier of the Roman or Byzantine era in southern Tunisia.