Libya Road Surface Classification from OpenStreetMap and AI Predictions

Approximately 0.2478 million kilometers of roads are mapped for Libya, with AI-derived estimates classifying 23.1418% as paved and 22.9852% as unpaved. The dataset is produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated in March 2026. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers.

Use Cases

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

Strengths

• Includes AI-derived predictions for road surface type, covering approximately 0.2478 million kilometers.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban classification layers.
• Provides detailed attributes including highway type, surface, smoothness, and urban classification.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Approximately 53.873% of road surface information is missing in the underlying OpenStreetMap 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 referenced papers.

Time Range

Mean timestamp of predictions is provided via the DL_mean_timestamp attribute.

Freshness

Last updated 2026-03-02 17:09:34.223836; freshness should be verified.

Geography

Libya