Road Surface Classification for Egypt from OSM and Satellite Imagery

Egypt's road network, covering approximately 0.6438 million kilometers, is classified into paved and unpaved surfaces. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and bridge status.
• Climate emissions assessment based on paved versus unpaved road distribution.
• GIS applications leveraging geospatial road data with urban/rural classification.

Strengths

• Provides explicit road length statistics: 0.0602 million km paved and 0.0169 million km unpaved.
• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban classification layers.
• Includes detailed attributes for classification, OSM tags, and urban context.

Limitations

• A significant portion (88.0308%) of road surface information is missing in the base OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers (GHSU, AFRICAPOLIS).

Collection Method

Surface classification derived using a hybrid deep learning approach, with methodology detailed in linked research papers.

Time Range

Mean timestamp of predictions is included (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:17:24.291203; freshness should be verified.

Geography

Egypt