Liberia Road Surface Classification from OpenStreetMap and AI

Approximately 43,400 km of roads are mapped in OpenStreetMap for Liberia. A hybrid deep learning model classifies these roads as paved or unpaved, estimating 3.792% are paved and 37.3214% are unpaved, while 58.8866% of road surface information is missing in OSM. The dataset is produced by HeiGIT and was last updated in March 2026.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis using road attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications utilizing geospatial road network data with urban/rural classifications.

Strengths

• Includes AI-derived surface classifications for paved and unpaved roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides specific statistics: 0.0016 million km paved, 0.0162 million km unpaved.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• A significant portion (58.8866%) of road surface information is missing in the base OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT).

Collection Method

Data originates from OpenStreetMap, augmented with deep learning predictions based on Mapillary imagery and combined with urban settlement layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:15:22.090496; freshness should be verified.

Geography

Liberia