Monaco Road Surface Classification from OSM and Deep Learning

Approximately 0.0004 million kilometers of roads are mapped for Monaco, with AI-derived estimates classifying 52.4849% as paved. The dataset is produced by HeiGIT, combining OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers, and was last updated in March 2026. It provides attributes for surface type, road classification, and urban context.

Use Cases

• Transportation planning based on paved and unpaved road classifications.
• Infrastructure analysis based on road attributes like highway type, surface, and width.
• Climate emissions assessment based on road surface and urban/rural classification.
• GIS applications for routing and network analysis based on geospatial road data.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban classification layers.
• Provides AI-derived surface estimates for approximately 0.0004 million kilometers of roads.
• Includes detailed attributes such as predicted class, OSM tags, and urban classification.

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.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

The mean timestamp of predictions used is included, but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:02:37.135042; freshness should be verified.

Geography

Monaco