Botswana Road Surface Classification from OpenStreetMap and AI

A geospatial dataset provides detailed information on road surfaces in Botswana, distinguishing between paved and unpaved roads. It contains approximately 0.1982 million kilometers of roads mapped in OpenStreetMap, with surface classifications derived using a hybrid deep learning approach. The dataset was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the distinction between paved and unpaved road networks.
• GIS applications for routing and logistics based on urban/rural classification and road attributes.

Strengths

• Contains approximately 0.1982 million kilometers of mapped roads.
• Provides AI-derived estimates for paved (0.0191 million km) and unpaved (0.0621 million km) roads.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in referenced papers.

Time Range

Mean timestamp of predictions is provided via the DL_mean_timestamp attribute.

Freshness

Last updated 2026-03-02 17:20:55.815857; freshness should be verified.

Geography

Botswana.