Guinea Road Surface Data with AI-Derived Paved and Unpaved Classifications

HeiGIT's dataset provides detailed road surface information for Guinea, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. It contains approximately 0.1897 million kilometers of mapped roads, with AI estimates classifying 2.6108% as paved and 27.2021% as unpaved. The dataset was last updated on March 2, 2026.

Use Cases

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

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers from GHSU and AFRICAPOLIS.
• Provides specific statistics: 0.1897 million km of roads mapped, with AI-derived percentages for paved (2.6108%) and unpaved (27.2021%) surfaces.
• Includes multiple classification schemes (OSM priority, DL priority) and urban/rural indicators for contextual analysis.

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.
• Approximately 70.1871% of road surface information is reported as missing in the original OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap (OSM) tags and augmented using a hybrid deep learning approach on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

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Freshness

Last updated 2026-03-02 17:21:07.729146; freshness should be verified.

Geography

Guinea