Israel Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides detailed road surface classifications for Israel, distinguishing paved and unpaved roads. It combines OpenStreetMap data with AI-derived predictions from Mapillary imagery, covering approximately 0.1301 million kilometers of roads. The dataset was last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the distinction between paved and unpaved road networks.
• GIS applications utilizing the geospatial road network data with urban/rural classification.

Strengths

• Includes AI-derived surface predictions for roads where OSM data is missing, adding 0.0024 million km of information.
• Provides multiple classification schemes (OSM-priority and DL-priority) for flexibility in analysis.
• Integrates urban/rural context from the GHSU and AFRICAPOLIS urban layers.

Limitations

• A significant portion (72.3038%) of road surface information is missing in the base OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and exact file sizes are unknown, which may limit suitability assessment.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Data originates from OpenStreetMap, augmented with deep learning predictions based on Mapillary imagery and combined with urban classification layers.

Time Range

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Freshness

Last updated 2026-03-02 17:18:38.092916; freshness should be verified.

Geography

Israel