###### Abstract  
This study focuses on an ad hoc machine-learning method for locating archaeological sites in arid environments. Pleiades (P1B) were uploaded to the cloud asset of the Google Earth Engine (GEE) environment because they are not yet available on the platform. The average of the SAR data was combined with the P1B image in the selected study area called Blad Talh at Gafsa, which is located in southern Tunisia. This pre-desert region has long been investigated as an important area of Roman civilization (106 BCE). The results show an accurate probability map with an overall accuracy and Kappa coefficient of 0.93 and 0.91, respectively, when validated with field survey data. The results of this research demonstrate, from the perspective of archaeologists, the capability of satellite data and machine learning to discover buried archaeological sites. This work shows that the area presents more archaeological sites, which has major implications for understanding the archaeological significance of the region. Remote sensing combined with machine learning algorithms provides an effective way to augment archaeological surveys and detect new cultural deposits. archaeological; machine learning; Google Earth Engine; remote sensing; fortified sites Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], W. The Use of Machine Learning and Satellite Imagery to Detect Roman Fortified Sites: The Case Study of Blad Talh (Tunisia Section) Article
of ancient remnants and can be used to assist with the digital reconstruction of ancient monuments and their environmental backgrounds. For example, in Mediterranean regions, such as Greece and Croatia, several studies have demonstrated excellent performance using a variety of passive and active remote sensing data (including multispectral imagery, thermal images, and UAV-based LiDAR data) to identify ancient Roman remains [13, 14, 15, 16, 17]. [PERSON] et al. [18] evaluated and debated the use of digital modelling tools in northwestern Iberia to identify and define Roman roads through GIS-based spatial analyses. In recent years, RS-based progressive archaeological studies have steadily integrated machine-learning algorithms that enable automated feature finding [19]. Most applications of these methods have focused on small-scale feature detection using LiDAR [20, 21, 22] or WorldView satellite data [8, 23, 24]. In the east, [PERSON] and [PERSON] [25] applied a random forest (RF) classifier to a multitemporal collection of ASTER imagery to classify probable anthrosols. In the Cholistan Desert, [PERSON] et al. [19] applied an RF classifier that integrates a multisensor, multitemporal machine learning approach to detecting archaeological mounds, and [PERSON] et al. [26, 27] used an artificial neural network model combined with a geographic information system (GIS) and remote sensing for surface water detection. Other studies have used multiple types of information to identify archaeological remains or threats to archaeological heritage, such as urban sprawl and archaeological looting [28, 29, 30]. Combined with machine learning algorithms, remote sensing provides an effective way to expand investigation areas and detect new cultural deposits [31, 32, 33, 34]. In particular, the application of this technology allows researchers to (1) investigate remote areas that are not easy to access due to harsh geography and (2) survey vast and complex environments (characterised, for example, by otherwise challenging topography). Platforms, such as Google Earth Engine (GEE) [35], provide a scalable, cloud-based, geospatial retrieval and processing platform, particularly in remote areas with little or almost no ground information. Google Earth Engine is a cloud-based platform that makes it simple to access high-performance computing resources for covering geospatial datasets. Once an algorithm is developed on GEE, users can produce regular data products or deploy interactive applications powered by Google Earth Engine resources.