Gambia Road Surface Classification from OpenStreetMap and AI Predictions

Road surface data for Gambia derived from OpenStreetMap (OSM) and augmented with deep learning predictions from Mapillary imagery. The dataset includes approximately 0.0282 million kilometers of mapped roads, with AI-derived estimates classifying 6.6068% as paved and 21.4047% as unpaved. It was created by HeiGIT and last updated in March 2026.

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 and urban/rural classification data.
• GIS applications for routing and logistics based on the geospatial road network.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers from GHSU and AFRICAPOLIS.
• Provides specific statistics: 0.0282 million km of roads mapped, with AI-derived estimates for paved (0.0019 million km) and unpaved (0.006 million km) segments.
• Includes multiple classification schemes (OSM priority, DL priority) and urban/rural indicators.

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 (71.9884%) of road surface information is reported as missing in the base OSM 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

null

Freshness

Last updated 2026-03-02 17:10:18.682903; freshness should be verified.

Geography

Gambia