# Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation

arXiv: https://arxiv.org/abs/2511.15875 · HTML: https://mlsanity.com/paper/2511.15875/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 20 of 20)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Unpaired Image-to-Image Translation via Neural Schr"odinger Bridge](https://mlsanity.com/paper/2305.15086/) (2023)
- [CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images](https://mlsanity.com/paper/2303.14126/) (2023)
- [Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation](https://mlsanity.com/paper/2004.10327/) (2020)
- [Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection](https://mlsanity.com/paper/2112.06104/) (2021)
- [Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps](https://mlsanity.com/paper/2310.12616/) (2023)
- [MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps](https://mlsanity.com/paper/2411.06971/) (2024)
- [Towards the automated large-scale reconstruction of past road networks from historical maps](https://mlsanity.com/paper/2202.04883/) (2022)
- [CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluation](https://mlsanity.com/paper/2505.09936/) (2025)
- [Few-Shot Segmentation of Historical Maps via Linear Probing of Vision Foundation Models](https://mlsanity.com/paper/2506.21826/) (2025)
- [Probabilistic road classification in historical maps using synthetic data and deep learning](https://mlsanity.com/paper/2410.02250/) (2024)
- [Leveraging LLMs and attention-mechanism for automatic annotation of historical maps](https://mlsanity.com/paper/2504.11050/) (2025)
- [Segmenting France Across Four Centuries](https://mlsanity.com/paper/2505.24824/) (2025)
- [DIGMAPPER: A Modular System for Automated Geologic Map Digitization](https://mlsanity.com/paper/2506.16006/) (2025)
- [SMOL-MapSeg: Show Me One Label as prompt](https://mlsanity.com/paper/2508.05501/) (2025)
- [Generative AI in Map-Making: A Technical Exploration and Its Implications for Cartographers](https://mlsanity.com/paper/2508.18959/) (2025)
- [Detecting Legend Items on Historical Maps Using GPT-4o with In-Context Learning](https://mlsanity.com/paper/2510.08385/) (2025)

## Abstract (arXiv)

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 a historical map corpus onto modern 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 an alternative manual stochastic degradation technique to emulate the visual uncertainty and noise, also known as aleatoric uncertainty, commonly observed in historical map scans. To quantitatively evaluate the effectiveness and applicability of our approach, the bootstrapped 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.