Vietnam Road Surface Data from OpenStreetMap and AI Predictions

Vietnam's road network is detailed in this dataset, which contains approximately 0.8443 million kilometers of roads from OpenStreetMap. The Heidelberg Institute for Geoinformation Technology (HeiGIT) augmented this data using a hybrid deep learning approach on Mapillary imagery to classify surfaces as paved or unpaved, with the dataset last updated in March 2026.

Use Cases

• Transportation planning based on road surface classification (paved/unpaved).
• Infrastructure analysis based on OSM attributes like highway type, width, and lanes.
• Climate emissions assessment based on road surface type and urban/rural classification.
• GIS applications for routing and logistics based on the geospatial road network.

Strengths

• Contains approximately 0.8443 million kilometers of mapped roads.
• Provides AI-derived surface classifications for paved (0.0657 million km) and unpaved (0.081 million km) roads.
• Augments OSM data with deep learning predictions from Mapillary imagery, adding 0.0123 million km of surface information.

Limitations

• Approximately 82.6209% (0.6975 million km) of road surface information is missing in the original OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is 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 on street-level imagery.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:15:57.039717; freshness should be verified.

Geography

Vietnam.