North Korea Road Surface Classification from OSM and Satellite Imagery

Approximately 0.1183 million km of roads are mapped for the Democratic People's Republic of Korea, with AI-derived estimates classifying only 1.2687% as paved. The dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers. It was last updated on March 2, 2026.

Use Cases

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

Strengths

• Covers approximately 0.1183 million km of roads in the region.
• Provides AI-derived surface classifications for paved and unpaved roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.

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 and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:13:32.124009; freshness should be verified.

Geography

Democratic People's Republic of Korea