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.
Highlights
The use of historical maps has a long tradition in archaeological research and has played an important role in a wide array of scientific disciplines (Chiang, Duan, Leyk, Uhl, & Knoblock, 2020)
We provide a first proof of concept for the automatic extraction of features of archaeological interest from large series of historical maps using deep learning (DL) approaches
Within the 47 maps of the Survey of India (SoI) analysed, 13,130 features were identified through the DL process before using the size threshold, 322 features in the early 1900s maps and 12,808 in the 1930s maps
Summary
The use of historical maps has a long tradition in archaeological research and has played an important role in a wide array of scientific disciplines (Chiang, Duan, Leyk, Uhl, & Knoblock, 2020). Since the late eighteenth century, the development of survey techniques on one side and political and ideological interests on the other pushed several European states to undertake systematic mapping of their own territories at an unprecedented scale and extension (Kent, Vervust, Demhardt, & Millea, 2020). This step change in European map production was almost immediately applied in their colonial dominions, starting during the nineteenth century, thereby reaching large parts of the world, as an inseparable companion of enlightenment, imperialism, agricultural intensification and the industrial revolution. In the aftermath of the First World War, imperial dominions extended through large parts of the Middle East and marked the beginning in the use of aerial survey techniques for large scale mapping
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