Namibia Road Surface Classification from OpenStreetMap and AI

HeiGIT's Namibia: Road Surface Data provides AI-derived classifications for approximately 201,600 kilometers of roads from OpenStreetMap. The dataset, last updated in March 2026, distinguishes paved from unpaved roads using a hybrid deep learning model applied to Mapillary imagery. It is augmented with urban classification layers from GHSU and AFRICAPOLIS.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis based on road attributes like surface, smoothness, and width.
• Climate emissions assessment based on road surface type and urban/rural classification.
• GIS applications for humanitarian access and logistics based on road network data.

Strengths

• Covers approximately 201,600 kilometers of roads in Namibia.
• Provides AI-derived estimates for 9.75% paved and 54.27% unpaved roads.
• Augments OSM data with deep learning predictions from Mapillary street-level imagery.

Limitations

• Approximately 72,500 kilometers (35.98%) of road surface information is missing in 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

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.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:27:28.917654; freshness should be verified.

Geography

Namibia.