The automated analysis of historical documents, particularly maps, has drastically benefited from advances in deep learning and its success across various computer vision applications.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

翻译：历史文献（尤其是地图）的自动分析已从深度学习的发展及其在各类计算机视觉应用中的成功中显著受益。然而，大多数基于深度学习的方法严重依赖大量标注训练数据，而这些数据通常难以获取，尤其是对于属于特定、同质制图领域（亦称语料库）的历史地图。生成适用于机器学习的高质量训练数据往往需要大量时间，并涉及广泛的人工劳动。尽管合成训练数据可以缓解现实样本的稀缺性，但其通常缺乏有效学习所需的亲和性（逼真度）和多样性（变化性）。通过将历史地图语料库的制图风格迁移至现代矢量数据，我们自举出几乎无限数量的合成历史地图，适用于对同质历史地图语料库进行土地覆盖解读等任务。我们提出了一种自动深度生成方法及一种替代性手动随机退化技术，以模拟历史地图扫描中常见的视觉不确定性和噪声（亦称 aleatoric 不确定性）。为定量评估我们方法的有效性和适用性，采用自举训练数据集，通过自构建图卷积网络对同质地图语料库进行领域自适应语义分割，从而全面评估数据自举方法的影响。