The 'Sacra Absentia' project, led by Simone Piazza (PI) with Giulia Anna Bianca Bordi (research fellow), is part of the PNRR (2022-2025) within the CHANGES program, Spoke 8, at University Ca' Foscari Venice. This study examines medieval and early modern pilgrims' accounts of Venice's religious heritage, informing the cataloguing and spatial reconstruction of lost or recontextualized liturgical furnishings. An online database georeferences these objects using contemporary maps and Jacopo de' Barbari's 1500 Venice view. A case study on Sant'Agnese, integrating published and unpublished sources, demonstrates the methodology's effectiveness in uncovering new historical insights.
This research, conducted within the framework of the In.Res.Agri project, explores the integration of advanced remote sensing and artificial intelligence techniques to investigate archaeological landscapes in the Campania region (Italy), with a focus on the regions of Irpinia and the Campanian Plain. The study utilises high-and very-high-resolution multispectral satellite imagery, processed through software such as eCognition, with the objective of identifying and analysing land divisions, including Roman centuriation, and other archaeological features. The methodology combines traditional topographical analysis with both semi-automated and fully automated deep learning approaches, notably the training of convolutional neural networks (CNNs) for the automated detection of linear features. Preliminary results demonstrate the software's capacity to validate known archaeological layouts while revealing previously unidentified structures, such as ancient road networks, in challenging terrain. The continuous enhancement of CNN models is intended to achieve fully automated detection, with the objective of accelerating landscape analysis and enabling field validation. The project under discussion demonstrates the considerable potential for the integration of geospatial analysis and AI-driven technologies to enhance our comprehension of archaeological landscapes. It is important to acknowledge, however, that such endeavours are not without their limitations, and that there is a necessity for ongoing refinement and on-site verification.
The contribution aims to present the unpublished data collected during a preventive archaeology excavation carried out in Nola-via Feudo, a suburban area S of the ancient city. The investigation shed light on a succession of dirt roads, dated from late republic to the end of the 5th century A.D., lined with graves. In the most recent phase, sealed by the so-called eruption of Pollena (472 A.D.), the beaten track was flanked by a ploughed field, covering partially the necropolis area along the street. The necropolis, partially violated in modern times, consisted of twenty-one burials with inhumation `alla cappuccina', enchytrism & oacute;s and in earthen pits. The preliminary study presented here increases our knowledge of the ager Nolanus to Late Antiquity, in the wake of the studies carried out in last decades.
The case study identified in Eastern Irpinia includes the territories belonging to the colony of Aeclanum, to the N, and ancient Compsa, to the S. It is a vast area bordered by the Calore River to the W, which reaches and exceeds the banks of the Ufita River to the E. It offers the opportunity to study a territory that has been little explored in relation to the topic of agrarian divisions, likely due to its decidedly complex geomorphological nature, characterized by a predominantly hilly landscape and rich hydrography, with limited plains. Furthermore, the historical interpretation of the entire territory is equally complex, for which the boundaries of each center can only be hypothesized. Following the indications of the Liber Coloniarum, which mentions divisions for both Aeclanum and Compsa, and considering the discovery of eight agrarian division stones, of which at least 6 dating to Gracchan period, an attempt is being made to reconstruct the agrarian divisions implemented in the territory through the analysis of historical cartography, aerial photographs and with the aid of machine-learning applications starting from high-resolution satellite images.
