Archeological prospection and 3D reconstruction are increasingly combined in large archeological projects that serve both site investigation and dissemination of results. This paper describes and validates a method for using multispectral imagery captured by unmanned aerial vehicles (UAVs), subsurface geophysical surveys, and stratigraphic excavations to evaluate the role of 3D semantic visualizations for the collected data. The information recorded by various methods will be experimentally reconciled using the Extended Matrix and other original open-source tools, keeping both the scientific processes that generated them and the derived data separate, transparent, and reproducible. This structured information makes immediately accessible the required variety of sources useful for interpretation and reconstructive hypotheses. The application of the methodology will use the first available data from a five-year multidisciplinary investigation project at Tres Tabernae, a Roman site near Rome, where numerous non-destructive technologies, as well as excavation campaigns, will be progressively deployed to explore the site and validate the approaches.

This project aimed to systematically investigate the archaeological remains of the imperial Domitian villa in Sabaudia (Italy), using different three-dimensional survey techniques. Particular attention in the research was paid to the identification and documentation of traces that buried structures left on the surface occupied by the villa, which extended for 46 hectares, an area that was fully covered with structures. Since a dense pine forest was planted during the 1940s and is currently covering the site, this contribution investigates particularly the correlation among the presence of cropmarks, identifiable with the processing of multispectral maps and vegetation indices from RGB images, and earthwork anomalies identified in a Digital Terrain Model (DTM) built, by utilizing a light detection and ranging (LiDAR) flight from an Unmanned Aerial Vehicle (UAV). The study demonstrates how the use of vegetation maps—calculated starting from RGB and multispectral aerial photos—can provide a more expeditious preliminary analysis on the position and extension of areas characterized by the presence of buried structures, but also that, in order to investigate in-depth a context in similar conditions, the most effective approach remains the one based on LiDAR technology. The integration between the two techniques may prove fruitful in limiting the extension of the areas to be investigated with terrestrial survey techniques.

This paper focuses on the potential of an integrated approach using aerial LiDAR, aerial and terrestrial photogrammetry, terrestrial laser scanning, and archaeological survey to detect the presence and configuration of lost medieval settlements under canopy. This approach was applied to the site of Altanum (Calabria, Italy), on the hill of Sant’Eusebio, completely covered by vegetation. Altanum was a large fortified settlement characterised by a long occupation, especially during the Byzantine and Norman-Swabian periods. The activity began by carrying out a LiDAR survey of the whole hill. The acquired LiDAR data were processed and filtered in order to obtain a DFM (Digital Feature Model) useful for the identification of features of archaeological interest. Several enhancement techniques were performed on DFM to increase the visibility of archaeological features. The features thus identified were subsequently surveyed through the use of terrestrial and aerial photogrammetry integrated with laser scanning to document the visible buildings. The most significant result of the study was to create a single GIS platform with the integration of all data in order to delineate the whole settlement layout, as well as to produce 2D and 3D datasets useful for the for knowledge and protection of the identified remains.

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.

Archaeological sites are not only of cultural and historical significance but also contribute to biodiversity conservation. Often marked by limited human disturbance and distinct ecological conditions, these areas serve as important refuges for various plant and animal species, playing a vital role in global conservation efforts. This review explores the relationship between archaeological sites and biodiversity through a systematic literature analysis using multiple databases and keywords. The goal was to evaluate the ecological value of these sites and identify key taxonomic groups, functional typologies, and geographic trends. A total of 241 studies were analysed, encompassing 1412 unique archaeological sites worldwide. Research spans all continents, though unevenly distributed. Most studies focus on biodiversity and conservation, followed by biodeterioration, while ecological, ethnographic, and sustainability‐focused work remains marginal. There is a strong bias towards flora, with 87% of studies emphasizing plant life. Among these, vascular plants dominate (over 79%), while lichens (14.4%), and especially mosses (3.2%), and algae (3.2%) are underrepresented. Among fauna, reptiles are the most studied group, followed by bats and non‐volant mammals, together accounting for nearly one‐third of animal‐focused studies. The remaining include birds (14%), amphibians (8%), fish (3%), molluscs (9.5%), and arthropods (12.5%). A subset of studies (32%) also documents the presence and ecological role of alien species within archaeological sites, highlighting both conservation challenges and management trade‐offs. Regarding archaeological site typologies, settlements (39%) and cultural sites (24%) receive the most attention, followed by funerary areas (18%) and defensive operas (17%). Functional infrastructures and public spaces are notably understudied, both comprising just 1% of studies, respectively. The integration of cultural heritage management with environmental sustainability is crucial for the conservation of biodiversity and historic landscapes. Conservation strategies should incorporate sustainable tourism practices, control invasive species, promote interdisciplinary collaboration, and implement climate adaptation measures. By recognizing the ecological importance of archaeological sites, policymakers and conservationists can develop holistic management approaches that safeguard both cultural heritage and biodiversity for future generations. Moreover, professionals and communicators can find new compelling narratives to disseminate their values to the wider public. Read the free Plain Language Summary for this article on the Journal blog.