Mozambique Road Surface Classification from OSM and Deep Learning

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Mozambique. It covers approximately 360,300 km of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The data is intended for transportation planning, infrastructure analysis, and climate emissions assessment.

Use Cases

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

Strengths

• Covers approximately 360,300 km of roads in Mozambique.
• Augments OSM data with deep learning predictions to address missing surface information for about 257,100 km.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural attributes.

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.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT).

Collection Method

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

Time Range

The dataset reflects OSM and imagery data up to its creation date; specific temporal coverage is not stated.

Freshness

Last updated 2026-03-02 17:15:25.271434; freshness should be verified.

Geography

Mozambique.