Road Surface Classification for the United Arab Emirates from OSM and AI

Approximately 0.1506 million kilometers of roads are mapped in this dataset, which classifies road surfaces as paved or unpaved across the United Arab Emirates. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, produced by the Heidelberg Institute for Geoinformation Technology (HeiGIT). It was last updated in March 2026.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis using road attributes like surface type, width, and lanes.
• Climate emissions assessment leveraging road surface data for modeling.
• GIS applications integrating urban classification attributes like rural/urban flags.

Strengths

• Provides AI-derived estimates for 0.0515 million km of paved and 0.0058 million km of unpaved roads.
• Includes multiple classification schemes (OSM-priority and DL-priority) and urban/rural flags.
• Augments OSM data with 0.0021 million km of additional surface information from Mapillary.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and file size are unknown, which may limit suitability assessment.
• A significant portion (61.9832%) of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with Mapillary imagery and predictions from HeiGIT.

Collection Method

Surface classification derived using a hybrid deep learning approach, combined with OSM tags and urban layers.

Time Range

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

Freshness

Last updated 2026-03-02 17:25:50.079748; freshness should be verified.

Geography

United Arab Emirates.