Holy See Road Surface Classification from OpenStreetMap and Deep Learning

0.0001 million kilometers of roads are mapped for the Holy See, with AI-derived estimates classifying 35.7427% as paved and 8.463% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers. It aims to address a 55.7943% gap in OSM surface information.

Use Cases

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

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban classification layers.
• Provides AI-derived surface classifications (paved/unpaved) for approximately 0.0001 million km of roads.
• Includes detailed OSM and urban classification attributes such as highway type, surface, smoothness, and urban/rural flag.

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.
• Row count and file size are unknown, which may limit suitability assessment.

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 the referenced arXiv papers.

Time Range

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

Freshness

Last updated 2026-03-02 17:27:31.795062; freshness should be verified.

Geography

Holy See (Vatican City).