Turkmenistan Road Surface Classification from OSM and AI Predictions

Turkmenistan's road network is classified by surface type using a hybrid deep learning approach on OpenStreetMap data. The dataset covers approximately 86,800 km of roads, with AI-derived estimates classifying 24.4% as paved and 14.9% as unpaved. It was created by HeiGIT and last updated in March 2026.

Use Cases

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

Strengths

• Includes AI-derived surface predictions for approximately 86,800 km of roads.
• Augments OSM data with deep learning predictions from Mapillary imagery to address data gaps.
• Provides multiple classification schemes (OSM-priority and DL-priority) for flexibility.

Limitations

• Approximately 60.7% of road surface information is missing in the underlying 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

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, as detailed in a referenced arXiv paper.

Time Range

Mean timestamp of predictions is included (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:19:58.355680; freshness should be verified.

Geography

Turkmenistan