Road Surface Classification for Malta from OpenStreetMap and Satellite Imagery

Approximately 5.1 km of roads are mapped in OpenStreetMap for Malta, with AI-derived estimates classifying 2.2 km as paved and 0.4 km as unpaved. This dataset, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT), provides detailed road surface information derived from a hybrid deep learning approach applied to OSM and Mapillary imagery, last updated in March 2026. It is augmented with urban classification layers and supports transportation and infrastructure analysis.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• 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 network analysis based on geospatial road data.

Strengths

• Includes AI-derived surface classifications for approximately 5.1 km of roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides combined classifications prioritizing both OSM tags and deep learning predictions.

Limitations

• Approximately 2.5 km (49.38%) of road surface information is missing in the base OSM data.
• Row count and file size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

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

null

Freshness

Last updated 2026-03-02 17:03:38.621878; freshness should be verified.

Geography

Malta