Road Surface Classification for Portugal from OpenStreetMap and AI

Portugal's road network is detailed in this dataset, which classifies approximately 0.4063 million kilometers of roads as paved or unpaved. The Heidelberg Institute for Geoinformation Technology (HeiGIT) created it by augmenting 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 using attributes like highway type, surface, and width.
• Climate emissions assessment informed by road surface material data.
• GIS applications leveraging geospatial road network data with urban/rural classification.

Strengths

• Includes AI-derived surface classifications for approximately 0.0858 million km of paved and 0.0428 million km of unpaved roads.
• Augments OpenStreetMap with additional 0.0032 million km of surface data from Mapillary imagery.
• Provides multiple classification schemes (OSM-priority and DL-priority) and urban/rural labels.

Limitations

• Approximately 0.2777 million km (68.3487%) 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 dataset size are unknown, which may limit suitability assessment.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in a referenced arXiv paper.

Time Range

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

Freshness

Last updated 2026-03-02 17:09:21.062854; freshness should be verified.

Geography

Portugal.