A NOVEL DATA AUGMENTATION METHOD TO ENHANCE THE TRAINING DATASET FOR ROAD EXTRACTION FROM SWISS HISTORICAL MAPS

Abstract

Long-term retrospective road data are required for various analyses (e.g., investigation of urban sprawl, analysis of road network evolution). Yet, it is challenging to extract roads from scanned historical maps due to their dissatisfying quality. Although deep learning has been exerting its superiority in image segmentation, its application to road extraction from historical maps is rarely seen in existing studies. Deep learning usually requires quite large amounts of training data, which is time-consuming and tedious to label. Data augmentation can to some extent solve this issue. The existing data augmentation techniques vary each training sample as a whole (e.g., rotation, flipping). But some features or symbols on maps will never occur in practice when they are rotated or flipped (e.g., numbers, labels). To solve this problem and to further improve the diversity of training samples, we propose a novel data augmentation method, which varies the target features instead of the whole training sample. The method is validated by applying it to road extraction from the historical Swiss Siegfried map. The experiment results show the effectiveness of the proposed method.