Road Surface Classification for Gabon from OpenStreetMap and AI Predictions

Approximately 70.1 thousand kilometers of roads are mapped in OpenStreetMap for Gabon, with AI-derived estimates classifying 4.6 thousand km as paved and 18.6 thousand km as unpaved. The dataset is produced by HeiGIT (Heidelberg Institute for Geoinformation Technology) and was last updated in March 2026. It combines OSM 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 using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road surface type and urban/rural classification.
• GIS applications for routing and accessibility studies based on the geospatial road network.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban classification layers.
• Provides AI-derived surface estimates for approximately 70.1 thousand kilometers of roads.
• Includes detailed OSM attributes such as name, highway type, surface, and smoothness.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 67% of road surface information is reported as missing in the underlying OSM data.
• Row count and dataset size are unknown, which may limit suitability assessment.

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, with methodology detailed in referenced arXiv papers.

Time Range

null

Freshness

Last updated 2026-03 02 17:06:04.760425; freshness should be verified.

Geography

Gabon