Brunei Darussalam Road Surface Classification from OSM and Satellite Imagery

Brunei Darussalam's road network is classified by surface type using a hybrid deep learning approach on OpenStreetMap and Mapillary data. The dataset covers approximately 0.006 million kilometers of roads, with AI-derived estimates for paved (0.0029 million km) and unpaved (0.0005 million km) segments. It was produced by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis using road attributes like highway type, surface, and width.
• Climate emissions assessment based on road surface and urban/rural classification.
• GIS applications leveraging geospatial road network data with AI-derived predictions.

Strengths

• Includes AI-derived surface classifications (paved/unpaved) for approximately 0.006 million km of roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides specific statistics, such as 48.8068% of mapped roads being paved.

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.
• Approximately 42.7076% of road surface information is reported as missing in the underlying OSM data.

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 referenced arXiv papers.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03 02 17:09:20.866298; freshness should be verified.

Geography

Brunei Darussalam.