Abstract
Quantitative analysis of historical urban sprawl in France before the 1970s is hindered by the lack of nationwide digital urban footprint data. This study bridges this gap by developing a scalable deep learning pipeline to extract urban areas from the Scan Histo historical map series (1925-1950), which produces the first open-access, national-scale urban footprint dataset for this pivotal period. Our key innovation is a dual-pass U-Net approach designed to handle the high radiometric and stylistic complexity of historical maps. The first pass, trained on an initial dataset, generates a preliminary map that identifies areas of confusion, such as text and roads, to guide targeted data augmentation. The second pass uses a refined dataset and the binarized output of the first model to minimize radiometric noise, which significantly reduces false positives. Deployed on a high-performance computing cluster, our method processes 941 high-resolution tiles covering the entirety of metropolitan France. The final mosaic achieves an overall accuracy of 73%, effectively capturing diverse urban patterns while overcoming common artifacts like labels and contour lines. We openly release the code, training datasets, and the resulting nationwide urban raster to support future research in long-term urbanization dynamics.
Abstract (translated)
在20世纪70年代之前法国城市扩张的历史定量分析受到全国范围内的数字化城市足迹数据缺乏的限制。本研究通过开发一种可扩展的深度学习管道来解决这一问题，该管道可以从1925年至1950年的Scan Histo历史地图系列中提取城市区域，从而生成首个公开访问的、国家规模的城市足迹数据集，以填补这段关键时期的数据空白。 我们的关键技术创新是一个双通道U-Net方法，专门设计用于处理历史地图中的高辐射度和风格复杂性。第一次通过初步训练集进行训练，生成一份初步的地图，该图识别出诸如文本和道路等混淆区域，并以此为依据指导目标数据增强工作。第二次使用精炼后的数据集以及第一阶段模型的二值化输出，以最小化辐射噪声，从而显著减少假阳性。 我们的方法部署在高性能计算集群上，在整个法国大都会范围内处理了941个高分辨率图块。最终的马赛克图像总体准确率为73%，有效捕捉到了各种城市模式，并克服了一些常见的问题，例如标签和轮廓线等人工痕迹。我们公开发布了代码、训练数据集以及生成的全国范围内的城市栅格，以支持未来有关长期城市化动态的研究工作。
URL
https://arxiv.org/abs/2510.02097
