Algeria Road Surface Classification from OSM and Satellite Imagery

Algeria's road network, comprising approximately 459,200 kilometers of mapped roads, is classified by surface type using a hybrid deep learning approach. The dataset, created by HeiGIT and last updated in March 2026, augments OpenStreetMap data with AI predictions from Mapillary imagery and urban layers. It distinguishes paved from unpaved roads and quantifies gaps in surface information.

Use Cases

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

Strengths

• Covers approximately 459,200 kilometers of roads in Algeria.
• Provides AI-derived estimates for 21.16% paved and 12.96% unpaved roads.
• Augments OSM data with deep learning predictions from Mapillary imagery.
• Includes urban classification attributes (rural/urban) from established settlement layers.

Limitations

• Approximately 65.87% 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 Mapillary imagery and GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach, with methodological details in linked arXiv 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 17:22:11.822459; freshness should be verified.

Geography

Algeria.