Burundi Road Surface Classification from OpenStreetMap and AI

Road surface data for Burundi distinguishes between paved and unpaved roads. The dataset contains approximately 0.0809 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates for surface type. It was published 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 smoothness.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for routing and logistics based on geospatial road network data.

Strengths

• Includes AI-derived surface classifications for approximately 0.0809 million km of roads.
• Combines OpenStreetMap data with deep learning predictions from Mapillary imagery.
• Provides multiple classification fields, including OSM and AI-prioritized versions.

Limitations

• Approximately 0.0383 million km (47.4108%) 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 sample data 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, as detailed in a referenced arXiv paper.

Time Range

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

Freshness

Last updated 2026-03-02 17:04:54.963092; freshness should be verified.

Geography

Burundi.