ABSTRACT Road intersection data have been used across a range of geospatial analyses. However, many datasets dating from before the advent of GIS are only available as historical printed maps. To be analyzed by GIS software, they need to be scanned and transformed into a usable (vector-based) format. Because the number of scanned historical maps is voluminous, automated methods of digitization and transformation are needed. Frequently, these processes are based on computer vision algorithms. However, the key challenges to this are (1) the low conversion accuracy for low quality and visually complex maps, and (2) the selection of optimal parameters. In this paper, we used a region-based deep convolutional neural network-based framework (RCNN) for object detection, in order to automatically identify road intersections in historical maps of several cities in the United States of America. We found that the RCNN approach is more accurate than traditional computer vision algorithms for double-line cartographic representation of the roads, though its accuracy does not surpass all traditional methods used for single-line symbols. The results suggest that the number of errors in the outputs is sensitive to complexity and blurriness of the maps, and to the number of distinct red-green-blue (RGB) combinations within them.

中文翻译：

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使用深度卷积神经网络从 USGS 历史地图系列中自动提取道路交叉点

摘要 道路交叉口数据已用于一系列地理空间分析。但是，许多可追溯到 GIS 出现之前的数据集仅作为历史印刷地图提供。要通过 GIS 软件进行分析，它们需要被扫描并转换为可用的（基于矢量的）格式。由于扫描的历史地图数量庞大，因此需要数字化和转换的自动化方法。通常，这些过程基于计算机视觉算法。然而，这方面的主要挑战是（1）低质量和视觉复杂地图的低转换精度，以及（2）最佳参数的选择。在本文中，我们使用基于区域的深度卷积神经网络框架 (RCNN) 进行对象检测，为了在美国几个城市的历史地图中自动识别道路交叉点。我们发现 RCNN 方法比用于道路双线制图表示的传统计算机视觉算法更准确，尽管其精度并未超过用于单线符号的所有传统方法。结果表明，输出中的错误数量对地图的复杂性和模糊度以及其中不同的红绿蓝 (RGB) 组合的数量很敏感。