The Roman centuriated landscape: conception, genesis and development as inferred from the Ager Tarraconensis case

Although centuriation was only one system of Roman land division, its impact on the landscape and its visibility in modern field arrangements make it the most commonly recognized expression of Roman landscapes. Centuriated grid systems are usually analyzed from a materialistic point of view and consequently regarded as an assertion of Roman dominance over conquered territories. In this sense, their productive function is clear. The hinterland of Tarraco (the ancient capital of the Roman province of Tarraconensis) offers one of the most clearly documented examples of multiple-grid centuriated systems. From 2006 to 2010, the Landscape Archaeology Research Group (GIAP) of the Catalan Institute of Classical Archaeology employed a wide array of digital and field methodologies at Tarraco to record the traces of centuriated land divisions and their Roman origin. Most importantly, these methods have allowed research to move beyond pure description of the traces to explore the concepts and ideas behind the making of a centuriated landscape. By using Tarraco as a case study, this article shows how centuriation was not only a system for dividing the land but also a conceptual appropriation of the landscape based on a strong mythical and religious backgroun

Potential of deep learning segmentation for the extraction of archaeological features from historical map series

Historical maps present a unique depiction of past landscapes, providing evidence for a wide range of information such as settlement distribution, past land use, natural resources, transport networks, toponymy and other natural and cultural data within an explicitly spatial context. Maps produced before the expansion of large‐scale mechanized agriculture reflect a landscape that is lost today. Of particular interest to us is the great quantity of archaeologically relevant information that these maps recorded, both deliberately and incidentally. Despite the importance of the information they contain, researchers have only recently begun to automatically digitize and extract data from such maps as coherent information, rather than manually examine a raster image. However, these new approaches have focused on specific types of information that cannot be used directly for archaeological or heritage purposes. This paper provides a proof of concept of the application of deep learning techniques to extract archaeological information from historical maps in an automated manner. Early twentieth century colonial map series have been chosen, as they provide enough time depth to avoid many recent large‐scale landscape modifications and cover very large areas (comprising several countries). The use of common symbology and conventions enhance the applicability of the method. The results show deep learning to be an efficient tool for the recovery of georeferenced, archaeologically relevant information that is represented as conventional signs, line‐drawings and text in historical maps. The method can provide excellent results when an adequate training dataset has been gathered and is therefore at its best when applied to the large map series that can supply such information. The deep learning approaches described here open up the possibility to map sites and features across entire map series much more quickly and coherently than other available methods, opening up the potential to reconstruct archaeological landscapes at continental scales

in : Jean-Paul Demoule (dir.) Des milieux et des hommes : méthodes d'étude en archéologies environnementales

International audienc

Des milieux et des hommes : méthodes d’études en archéologies environnementales

International audienc