Myanmar Road Surface Data with AI-Derived Paved and Unpaved Classifications

OpenStreetMap road data for Myanmar, augmented with AI-derived surface classifications from Mapillary imagery. The dataset covers approximately 326,300 kilometers of roads, with AI predictions providing surface type for about 18% of the network. It was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length data.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on paved versus unpaved road networks.
• GIS applications leveraging the geospatial road network and urban/rural classification.

Strengths

• Provides AI-derived surface classification for approximately 58,900 kilometers of roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and global urban settlement layers.
• Includes detailed OSM attributes such as name, highway type, surface, and width.

Limitations

• Approximately 267,400 kilometers (81.9%) of road surface information is missing from the base 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, 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, as detailed in the referenced arXiv paper.

Time Range

null

Freshness

Last updated 2026-03-02 17:29:57.392736; freshness should be verified.

Geography

Myanmar