Historical maps are unique and valuable archives that document geographic features across different time periods. However, automated analysis of historical map images remains a significant challenge due to their wide stylistic variability and the scarcity of annotated training data. Constructing linked spatio-temporal datasets from historical map time series is even more time-consuming and labor-intensive, as it requires synthesizing information from multiple maps. Such datasets are essential for applications such as dating buildings, analyzing the development of road networks and settlements, studying environmental changes etc. We present MapSAM2, a unified framework for automatically segmenting both historical map images and time series. Built on a visual foundation model, MapSAM2 adapts to diverse segmentation tasks with few-shot fine-tuning. Our key innovation is to treat both historical map images and time series as videos. For images, we process a set of tiles as a video, enabling the memory attention mechanism to incorporate contextual cues from similar tiles, leading to improved geometric accuracy, particularly for areal features. For time series, we introduce the annotated Siegfried Building Time Series Dataset and, to reduce annotation costs, propose generating pseudo time series from single-year maps by simulating common temporal transformations. Experimental results show that MapSAM2 learns temporal associations effectively and can accurately segment and link buildings in time series under limited supervision or using pseudo videos. We will release both our dataset and code to support future research.

翻译：历史地图是记录不同时期地理特征的独特且珍贵的档案。然而，由于历史地图图像风格差异巨大且标注训练数据稀缺，其自动化分析仍面临重大挑战。从历史地图时间序列构建关联的时空数据集则更为耗时费力，因其需要综合多幅地图的信息。此类数据集对于建筑物年代判定、道路网络与聚落发展分析、环境变化研究等应用至关重要。本文提出MapSAM2，一个用于自动分割历史地图图像与时间序列的统一框架。基于视觉基础模型构建，MapSAM2可通过少量样本微调适配多种分割任务。我们的核心创新在于将历史地图图像与时间序列均视为视频处理。对于单幅图像，我们将一组图块作为视频处理，使记忆注意力机制能够整合来自相似图块的上下文线索，从而提升几何精度，尤其对于面状要素。针对时间序列，我们引入了标注的Siegfried建筑时间序列数据集，并为降低标注成本，提出通过模拟常见时间变换从单年度地图生成伪时间序列。实验结果表明，MapSAM2能有效学习时间关联，在有限监督下或使用伪视频时，可准确分割并关联时间序列中的建筑物。我们将公开数据集与代码以支持后续研究。