  Road intersections data have been used across different geospatial
applications and analysis. The road network datasets dating from pre-GIS years
are only available in the form of historical printed maps. Before they can be
analyzed by a GIS software, they need to be scanned and transformed into the
usable vector-based format. Due to the great bulk of scanned historical maps,
automated methods of transforming them into digital datasets need to be
employed. Frequently, this process is based on computer vision algorithms.
However, low conversion accuracy for low quality and visually complex maps and
setting optimal parameters are the two challenges of using those algorithms. In
this paper, we employed the standard paradigm of using deep convolutional
neural network for object detection task named region-based CNN for
automatically identifying road intersections in scanned historical USGS maps of
several U.S. cities. We have found that the algorithm showed higher conversion
accuracy for the double line cartographic representations of the road maps than
the single line ones. Also, compared to the majority of traditional computer
vision algorithms RCNN provides more accurate extraction. Finally, the results
show that the amount of errors in the detection outputs is sensitive to
complexity and blurriness of the maps as well as the number of distinct RGB
combinations within them.
