Djibouti Road Surface Classification from OpenStreetMap and AI

Djibouti's road network is detailed in this dataset, which classifies surfaces as paved or unpaved. The Heidelberg Institute for Geoinformation Technology (HeiGIT) created it by augmenting OpenStreetMap data with deep learning predictions from Mapillary imagery. It was last updated on March 2, 2026.

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 data for vehicle emission modeling.
• GIS applications utilizing the geospatial road network data for mapping and analysis.

Strengths

• Provides AI-derived estimates for approximately 8.2 km of roads, with 19.25% paved and 26.13% unpaved.
• Augments OpenStreetMap data with an additional 0.1 km of surface information from Mapillary imagery.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural classification.

Limitations

• A significant portion (54.62%) of road surface information is missing in the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• 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, as detailed in the referenced arXiv paper.

Time Range

The mean timestamp of predictions used is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:01:06.626449; freshness should be verified.

Geography

Djibouti.