Lebanon Road Surface Classification with AI-Derived Estimates

Approximately 0.0517 million kilometers of roads are mapped for Lebanon, with AI-derived estimates classifying 19.1793% as paved and 3.8679% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It is intended to support transportation planning, infrastructure analysis, and climate emissions assessment.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission models.
• GIS applications for urban/rural classification using the urban attribute.
• Data augmentation for OSM by filling missing surface information with AI predictions.

Strengths

• Provides AI-derived surface estimates for 0.0099 million km of paved and 0.002 million km of unpaved roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.
• Includes specific AI feature columns like pred_class, pred_label, and combined classification priorities.
• Offers detailed OSM and urban classification attributes for contextual analysis.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Approximately 76.9528% of road surface information is reported as missing in the original OSM data.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology), using OpenStreetMap, Mapillary, GHSU, and AFRICAPOLIS.

Collection Method

Surface classification derived using a hybrid deep learning approach on street-level imagery, combined with OSM tags.

Time Range

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

Freshness

Last updated 2026-03-17 13:11:32.522627; freshness should be verified.

Geography

Lebanon.