  Transportation infrastructure, such as road or railroad networks, represent a
fundamental component of our civilization. For sustainable planning and
informed decision making, a thorough understanding of the long-term evolution
of transportation infrastructure such as road networks is crucial. However,
spatially explicit, multi-temporal road network data covering large spatial
extents are scarce and rarely available prior to the 2000s. Herein, we propose
a framework that employs increasingly available scanned and georeferenced
historical map series to reconstruct past road networks, by integrating
abundant, contemporary road network data and color information extracted from
historical maps. Specifically, our method uses contemporary road segments as
analytical units and extracts historical roads by inferring their existence in
historical map series based on image processing and clustering techniques. We
tested our method on over 300,000 road segments representing more than 50,000
km of the road network in the United States, extending across three study areas
that cover 53 historical topographic map sheets dated between 1890 and 1950. We
evaluated our approach by comparison to other historical datasets and against
manually created reference data, achieving F-1 scores of up to 0.95, and showed
that the extracted road network statistics are highly plausible over time,
i.e., following general growth patterns. We demonstrated that contemporary
geospatial data integrated with information extracted from historical map
series open up new avenues for the quantitative analysis of long-term
urbanization processes and landscape changes far beyond the era of operational
remote sensing and digital cartography.
