HIRONEX is a python tool for automatic, fully unsupervised extraction of historical road networks from historical maps. HIRONEX reads a shapefile containing the modern road network, and a scanned & georeferenced historical map from year T, covering the same area, in the same spatial reference system. Then, HIRONEX samples the color information from the historical map, and calculates a probability (road overlap indicator - ROI) for each modern road segment of overlapping with a road symbol in the historical map. Then, 1d cluster analysis is applied to extract road segments that are likely to have existed in the year T of the historical map. The ROI and resulting cluster labels are then appended to the input shapefile, which can be visualized directly in the script.

The HIRONEX output is not perfect, as HIRONEX is work in progress and is currently being improved. There are a few user-set parameters (see script), that should be tested in order to optimize results. While sensitivity analyses have shown that the results are largely invariant to these parameters, tweaking them can greatly improve processing time.

Reference:

Uhl, J. H., Leyk, S., Chiang, Y. Y., & Knoblock, C. A. (2022). Towards the automated large-scale reconstruction of past road networks from historical maps. Computers, Environment and Urban Systems, 94, 101794. https://doi.org/10.1016/j.compenvurbsys.2022.101794