Historical maps provide useful spatio-temporal information on the Earth's surface before modern earth observation techniques came into being. To extract information from maps, neural networks, which gain wide popularity in recent years, have replaced hand-crafted map processing methods and tedious manual labor. However, aleatoric uncertainty, known as data-dependent uncertainty, inherent in the drawing/scanning/fading defects of the original map sheets and inadequate contexts when cropping maps into small tiles considering the memory limits of the training process, challenges the model to make correct predictions. As aleatoric uncertainty cannot be reduced even with more training data collected, we argue that complementary spatio-temporal contexts can be helpful. To achieve this, we propose a U-Net-based network that fuses spatio-temporal features with cross-attention transformers (U-SpaTem), aggregating information at a larger spatial range as well as through a temporal sequence of images. Our model achieves a better performance than other state-or-art models that use either temporal or spatial contexts. Compared with pure vision transformers, our model is more lightweight and effective. To the best of our knowledge, leveraging both spatial and temporal contexts have been rarely explored before in the segmentation task. Even though our application is on segmenting historical maps, we believe that the method can be transferred into other fields with similar problems like temporal sequences of satellite images. Our code is freely accessible at https://github.com/chenyizi086/wu.2023.sigspatial.git.

翻译：历史地图提供了现代地球观测技术诞生前地球表面的珍贵时空信息。为从地图中提取信息，近年来广受欢迎的神经网络已取代传统手工地图处理方法与繁琐的人工操作。然而，原始图幅的绘制/扫描/褪色缺陷以及因训练过程内存限制而将地图切分为小图块时导致的上下文不足，所固有的数据依赖不确定性（即偶然不确定性）对模型正确预测构成挑战。由于即使收集更多训练数据也无法减少偶然不确定性，我们认为互补的时空上下文可发挥积极作用。为此，我们提出基于U-Net的融合时空特征与交叉注意力Transformer的网络U-SpaTem，通过在更大空间范围及时间序列图像中聚合信息。该模型性能优于仅使用时间或空间上下文的现有最优模型。与纯视觉Transformer相比，本模型更轻量高效。据我们所知，在分割任务中同时利用空间与时间上下文的探索尚属罕见。尽管本应用聚焦历史地图分割，但该方法可迁移至卫星图像时间序列等具有类似问题的其他领域。本模型代码已开源发布于https://github.com/chenyizi086/wu.2023.sigspatial.git。