Road network in the DR Congo 1966

African road network data

Road network in the DR Congo 1966

African road network data

Joint work with Philipp Hunziker

Digitizing African road network data (1966-2017)

We leverage a custom fully convolutional neural network to collect road network data for post-colonial Africa from physical maps and transform the spatial data into a time-variant digital road atlas akin to Google maps. Our primary source consists in the Michelin maps corpus, which is a collection of large topographical maps at a resolution of 1:4,000,000. Each map shows detailed information on road infrastructure with a consistent cartographic symbology for about a third of the continent. While coverage before the 1960s is sporadic, Michelin has covered the entire African continent at intervals of approximately 5 years beginning in the mid 1960s. This makes the Michelin corpus an unparalleled source for time-variant road-network information. In total we digitize 34 map sheets, which combine into 23 maps of the entire continent. Using a custom convolutional neural network for pixel classification, we an digitize the maps in a comparatively cheap, replicable, and scaleable manner.

Fully convolutional neural network

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Carl Müller-Crepon
Postdoctoral researcher in Political Science

My research focuses on state building, development, and conflict in 20th century Africa.