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

Published version
Peer-reviewed

Type

Article

Change log

Abstract

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.

Description

Keywords

4301 Archaeology, 4303 Historical Studies, 43 History, Heritage and Archaeology, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Generic health relevance, 15 Life on Land

Journal Title

Archaeol Prospect

Conference Name

Journal ISSN

1075-2196
1099-0763

Volume Title

28

Publisher

Wiley
Sponsorship
European Research Council (648609)
European Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (746446)
Biotechnology and Biological Sciences Research Council (BB/P027970/1)