Eswatini Road Surface Data with AI-Derived Paved and Unpaved Classifications

Eswatini road network data from OpenStreetMap, enhanced with deep learning predictions to classify paved and unpaved surfaces. The dataset includes approximately 0.033 million kilometers of roads, with AI-derived estimates for 6.8539% paved and 28.3574% unpaved. It was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length estimates.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging the distinction between paved and unpaved roads.
• GIS applications for routing and network analysis based on geospatial road data.

Strengths

• Includes AI-derived surface classifications for paved and unpaved roads.
• Combines OpenStreetMap data with deep learning predictions from Mapillary imagery.
• Contains approximately 0.033 million kilometers of mapped roads.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• About 64.7888% of road surface information is reported as missing in the original OSM data.

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 the referenced papers.

Time Range

Mean timestamp of predictions is included (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:01:45.351080; freshness should be verified.

Geography

Eswatini (region).