Semantic segmentation of historical maps using Self-Constructing Graph Convolutional Networks

Historical maps represent an invaluable memory which should be preserved. Such kind of maps are, however, mostly scanned and stored as raster graphics which do not contain semantic information in a machine-readable form. To achieve a machine-readable state, an often expensive human intervention is needed in a fully manual or semi-automatic fashion. An automatic interpretation and a feature extraction is then inevitable for a map digitization and vectorization. Automatic approaches showed more and more convincing and promising results on challenging map corpora avoiding human interaction. This paper deals with the semantic segmentation of historical maps based on Graph Convolutional Networks (GCNs) to capture long-range dependencies between image features. This allows for an extension of the receptive field of Convolutional Neural Networks (CNNs) restricted on local dependencies. A Self-Constructing Graph (SCG) module has been applied to automatically induce the structure of the GCN. We performed experiments revealing promising results where our approach achieved an Mean Intersection over Union (mIoU) of (Formula presented.), outperforming a state-of-the-art CNN dedicated to the semantic segmentation of historical maps.