The introduction of generative AI has the potential to radically transform various fields of research, including archaeology. This study explores the potential of generative AI, specifically ChatGPT, in developing a computer application for analyzing aerial and satellite images to detect archaeological anomalies. The main focus was not on developing the application itself but on evaluating ChatGPT’s effectiveness as an IT assistant for humanistic researchers. Starting with a simple prompt to analyze a multispectral orthophoto, the application was developed through successive iterations, improved through continuous interactions with ChatGPT. Various technical and methodological challenges were addressed, leading to the creation of a functional application with multiple features, including various analysis methods and tools. This process demonstrated how the use of large language models (LLMs) can break down the barriers between humanities and computer science disciplines, enabling researchers without programming skills to develop complex applications in a short time.

This study investigates the applicability of drone technology in examining Stracciacappe, a minor archaeological site through low-altitude aerial photography. Using multispectral and thermal sensors mounted on DJI Phantom Multispectral and DJI Mavic Enterprise Advanced drones, several flight missions were conducted in November 2020, May 2021, and April 2022. The effectiveness of analyzing multispectral and thermal raw images was limited by the area’s irregular vegetation, which hindered the clear detection of archaeological anomalies. However, microtopographic analysis employing various visualization techniques revealed significant traces, aligning with the site’s description found in numerous documentary sources. This includes the identification of two distinct areas within the castrum: the elevated cassarum and the burgus, along with potential traces of defensive structures within these areas. Drone analysis delineated a cassarum comprising a tower, palatium, and defensive walls, while the burgus seemed devoid of buildings, supporting the notion of a village primarily constructed with perishable materials. Thus, the study highlights the importance of using diverse sensor-based drone analyses to enhance archaeological investigations at minor sites.

This study introduces a methodology for the improvement of the visibility of archaeological features using an open-source probabilistic machine learning framework applied to UAV LiDAR data from the Torre Castiglione site in Apulia, Italy. By leveraging a Random Forest classification algorithm embedded in an open-source software, the approach processes dense LiDAR point clouds to segment out vegetation from the ground and the structures. Key steps include training the classifier, generating digital terrain models, digital feature models, and digital surface models, and enhancing the visibility of archaeological features. This method has proven effective in improving the interpretation of archaeological sites, revealing previously hidden or difficult-to-access microtopographic and structural details, such as the defensive structures, terraces, and ancient paths of the Torre Castiglione site. The results underline this methodology’s ease of use in uncovering archaeological landscapes under a dense canopy. Moreover, the study emphasises the benefits of using open-source tools to enhance the documentation and analysis of remote or difficult archaeological sites.

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes.