Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation

2025-11-19

AI Summary

Semantic segmentation of historical maps suffers from scarce real annotated data and insufficient realism/diversity in existing synthetic datasets. Method: We propose a style-consistent automated synthesis framework that integrates deep generative models with controllable stochastic degradation strategies to transfer original cartographic styles while injecting visually plausible uncertainty and noise governed by scanning degradation physics—enabling unlimited, photorealistic, and diverse training sample generation. A lightweight graph convolutional network is further designed to support domain-adaptive segmentation. Results: The synthesized data significantly improves segmentation accuracy on homogeneous historical maps (mIoU ↑12.3%), outperforming state-of-the-art synthetic methods in realism, diversity, and generalization. This framework serves as a scalable data engine for analyzing rare historical document images.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

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The automated analysis of historical documents, particularly maps, has drastically benefited from advances in deep learning and its success across various computer vision applications.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.

Problem

• Addressing annotated data scarcity for historical map segmentation using deep learning
• Generating realistic synthetic training data to overcome manual annotation limitations
• Quantifying effectiveness of synthetic data for domain-adaptive semantic segmentation

Innovation

• Generating synthetic historical maps via style transfer
• Emulating visual uncertainty with deep generative models
• Using self-constructing graph networks for segmentation evaluation