Road Surface Classification for Laos, Derived from OpenStreetMap and Deep Learning

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Laos. It covers approximately 109,200 kilometers of roads from OpenStreetMap, with deep learning predictions augmenting missing surface data. The data is intended for transportation planning, infrastructure analysis, and GIS applications.

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 material and urban/rural classification.
• GIS applications for routing and logistics using the geospatial road network data.

Strengths

• Provides AI-derived surface classifications for approximately 109,200 km of roads.
• Includes multiple classification schemes (OSM-priority and DL-priority) for comparison.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery.
• Integrates urban classification layers (GHSU, AFRICAPOLIS) for contextual analysis.

Limitations

• Approximately 63,200 km (57.9%) 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

Heidelberg Institute for Geoinformation Technology (HeiGIT).

Collection Method

Derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03-02 17:22:07.028633.

Geography

Lao People's Democratic Republic (Laos).