Ethiopia Road Surface Classification from OpenStreetMap and AI

Approximately 0.477 million kilometers of roads are mapped in OpenStreetMap for Ethiopia. The dataset provides AI-derived surface classifications, estimating 7.6% paved and 16.6% unpaved roads, while 75.8% of OSM data lacks surface information. It was created by HeiGIT using a hybrid deep learning approach on Mapillary imagery and was last updated in March 2026.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on road surface material and urban/rural classification.
• GIS applications for routing and logistics using the geospatial road network.

Strengths

• Covers approximately 0.477 million kilometers of roads in Ethiopia.
• Provides AI-derived predictions for road surface where OpenStreetMap data is missing.
• Includes a wide range of OSM attributes such as name, highway type, surface, and width.

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.
• A significant portion (75.8%) of the base OSM data lacks surface information.

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 on street-level imagery.

Time Range

null

Freshness

Last updated 2026-03 02 17:04:21.993409; freshness should be verified.

Geography

Ethiopia