Morocco Road Surface Classification from OpenStreetMap and AI

0.5282 million kilometers of roads in Morocco are mapped, with AI-derived surface classifications for paved and unpaved segments. The dataset is created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) using a hybrid deep learning approach on Mapillary imagery and OSM data, last updated in March 2026. It aims to address a 67.479% gap in surface information within OpenStreetMap.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using road attributes like highway type, surface, and width.
• Climate emissions assessment leveraging road network data for modeling.
• GIS applications utilizing geospatial road data with urban/rural classification.

Strengths

• Covers approximately 0.5282 million kilometers of roads in Morocco.
• Provides AI-derived surface classifications for 0.1088 million km paved and 0.0629 million km unpaved roads.
• Augments OSM data with deep learning predictions from Mapillary street-level imagery.

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.

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.

Time Range

Mean timestamp of predictions is provided via the DL_mean_timestamp column.

Freshness

Last updated 2026-03-02 17:12:19.893488; freshness should be verified.

Geography

Morocco