Deep learning for historical cadastral maps digitization: overview, challenges and potential

Abstract

Cartographic heritage of historical cadastral maps represent remarkable geospatial data. Historical cadastral maps are generally regarded as an essential part of the land management infrastructure (buildings, streets, canals, bridges, etc.). Today these cadastral maps are still in use in a digital raster form (scanned maps). Digitization of cadastral maps is time consuming and it is a challenge for scientists and engineers to find ways to automatically convert raster into vector maps. The process of map digitization typically involves several stages: preprocessing, visual object detection and classification, vector representation postprocessing and extracting information from text. Although neural networks have had a long history of use in the domain, their applications remain limited to extracting the information from text. Recent convergence of advancements in the domains of training deep neural networks (DNN) and GPU hardware allowed DNNs to achieve state-of-the-art results in computer vision applications, beyond hand-written text recognition. This paper provides an overview of different approaches to historical cadastral maps digitization, focusing of the challenges and the potential of using deep neural networks in map digitization.