Malaysia Road Surface Classification from OSM and Deep Learning

HeiGIT's dataset provides detailed road surface information for Malaysia, classifying roads as paved or unpaved. It contains approximately 0.4788 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates covering 15.9342% paved and 13.4757% unpaved. The data was last updated on March 2, 2026, and combines OSM data with deep learning predictions from Mapillary imagery.

Use Cases

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

Strengths

• Covers approximately 0.4788 million kilometers of roads in Malaysia.
• Provides AI-derived surface estimates for 29.4099% of the OSM road network.
• Augments OSM data with 0.0023 million kilometers of additional surface information from Mapillary.

Limitations

• Approximately 70.5901% 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.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

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

Freshness

Last updated 2026-03-02 17:16:14.313617.

Geography

Malaysia.