Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation

• URL: http://arxiv.org/abs/2511.15875v1
• Date: Wed, 19 Nov 2025 21:02:40 GMT
• Title: Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation
• Authors: Lukas Arzoumanidis, Julius Knechtel, Jan-Henrik Haunert, Youness Dehbi,
• Abstract summary: Most deep learning-based methods rely on large amounts of annotated training data, which are typically unavailable for historical maps.<n>We bootstrap an effectively unlimited number of synthetic historical maps suitable for tasks such as land-cover interpretation.<n>To evaluate the effectiveness and applicability of our approach, the generated training datasets were employed for domain-adaptive semantic segmentation.
• Score: 0.48998185508205744
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The automated analysis of historical documents, particularly maps, has drastically benefited from advances in deep learning and its success across various computer vision applications. However, most deep learning-based methods heavily rely on large amounts of annotated training data, which are typically unavailable for historical maps, especially for those belonging to specific, homogeneous cartographic domains, also known as corpora. Creating high-quality training data suitable for machine learning often takes a significant amount of time and involves extensive manual effort. While synthetic training data can alleviate the scarcity of real-world samples, it often lacks the affinity (realism) and diversity (variation) necessary for effective learning. By transferring the cartographic style of an original historical map corpus onto vector data, we bootstrap an effectively unlimited number of synthetic historical maps suitable for tasks such as land-cover interpretation of a homogeneous historical map corpus. We propose an automatic deep generative approach and a alternative manual stochastic degradation technique to emulate the visual uncertainty and noise, also known as data-dependent uncertainty, commonly observed in historical map scans. To quantitatively evaluate the effectiveness and applicability of our approach, the generated training datasets were employed for domain-adaptive semantic segmentation on a homogeneous map corpus using a Self-Constructing Graph Convolutional Network, enabling a comprehensive assessment of the impact of our data bootstrapping methods.

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