Deep learning ancient map segmentation to assess historical landscape changes

Ancient geographical maps are our window into the past for understanding the spatial dynamics of last centuries. This paper proposes a novel approach to address this problem using deep learning. Convolutional neural networks (CNNs) are today the state-of-the-art methods in handling a variety of problems in the fields of image processing. The Cassini map, created in the eighteenth century, is used to illustrate our methodology. This approach enables us to extract the surfaces of classes of lands in the Cassini map: forests, heaths, arboricultural, and hydrological. The evolution of land use between the end of the eighteenth century andtoday was quantified by comparison with Corine Land Cover (CLC) database. For the Rhone watershed, the results show that forests, arboriculture, and heaths are more extensive on the CLC map, in contrast to the hydrological network. These unprecedented results are new findings that reveal the major anthropo-climatic changes.Semantic segmentation allows us to identify several land use patterns from a cartographic support item such as the Cassini map.Semantic segmentation reduces the analysis time of the map by a factor of approximately 10 compared with an entirely manual segmentation, while maintaining an average accuracy equivalent to 90%.Our results illustrate a climatic and anthropic forcing on the Rhône watershed that significantly modified the landscape compared with today.