Towards Historical Map Analysis Using Deep Learning Techniques

Ladislav Lenc and Josef Baloun and Jiří Martínek and Ladislav Lenc
AIAI (2023)


This paper presents methods for automatic analysis of historical cadastral maps. The methods are developed as a part of a complex system for map digitisation, analysis and processing. Our goal is to detect important features in individual map sheets to allow their further processing and connecting the sheets into one seamless map that can be better presented online. We concentrate on detection of the map frame, which defines the important segment of the map sheet. Other crucial features are so-called inches that define the measuring scale of the map. We also detect the actual map area. We assume that standard computer vision methods can improve results of deep learning methods. Therefore, we propose novel segmentation approaches that combine standard computer vision techniques with neural nets (NNs). For all the above-mentioned tasks, we evaluate and compare our so-called “Combined methods” with state-of-the-art methods based solely on neural networks. We have shown that combining the standard computer vision techniques with NNs can outperform the state-of-the-art approaches in the scenario when only little training data is available. We have also created a novel annotated dataset that is used for network training and evaluation. This corpus is freely available for research purposes which represents another contribution of this paper.



@inproceedings{lenc2023towards, title={Towards Historical Map Analysis Using Deep Learning Techniques}, author={Lenc, Ladislav and Baloun, Josef and Mart{\'\i}nek, Ji{\v{r}}{\'\i} and Kr{\'a}l, Pavel}, booktitle={IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages={173--185}, year={2023}, organization={Springer} }
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