Zambia Road Surface Classification with AI-Derived Estimates

Zambia's road network is detailed in this dataset, which distinguishes paved and unpaved roads. It contains approximately 0.3549 million kilometers of roads mapped in OpenStreetMap, with AI-derived surface classifications for about 33% of them. The data was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis based on attributes like highway type, surface, and width.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for urban/rural classification based on the urban attribute.

Strengths

• Includes AI-derived surface predictions for roads where OpenStreetMap data is missing.
• Provides specific statistics on road length, including 0.021 million km paved and 0.0966 million km unpaved.
• Combines data from multiple sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.

Limitations

• A significant portion (66.8423%) of road surface information is missing from the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:18:06.445959; freshness should be verified.

Geography

Zambia