# Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks

arXiv: https://arxiv.org/abs/2007.07404 · HTML: https://mlsanity.com/paper/2007.07404/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems](https://mlsanity.com/paper/2401.10279/) (2024)
- [Towards the automated large-scale reconstruction of past road networks from historical maps](https://mlsanity.com/paper/2202.04883/) (2022)
- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [Identifying Wetland Areas in Historical Maps using Deep Convolutional Neural Networks](https://mlsanity.com/paper/2108.04107/) (2021)
- [Segmenting France Across Four Centuries](https://mlsanity.com/paper/2505.24824/) (2025)
- [Evolving Thematic Map Design in Academic Cartography: A Thirty-Year Study Based on Multilingual Journals](https://mlsanity.com/paper/2604.22539/) (2026)

## Abstract (arXiv)

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.