San Marino Road Surface Classification from OSM and Deep Learning

Road surface data for San Marino derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary imagery. The dataset, created by HeiGIT, includes approximately 0.7 km of mapped roads, with AI-derived estimates classifying 42.9% as paved and 4.6% as unpaved. It was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and highway classification.
• Infrastructure analysis using attributes like surface, smoothness, width, and bridge status.
• Climate emissions assessment informed by road material (paved vs. unpaved) data.
• GIS applications leveraging geospatial road network data with urban/rural classification.

Strengths

• Integrates multiple data sources: OSM, deep learning predictions from Mapillary, and urban layers from GHSU and AFRICAPOLIS.
• Provides AI-derived surface classifications (paved/unpaved) for roads where OSM data is missing.
• Includes detailed OSM and urban classification attributes for contextual analysis.

Limitations

• Row count and file size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 52.5% of road surface information is reported as missing in the base OSM data.

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, with methodology detailed in a linked arXiv paper.

Time Range

The mean timestamp of predictions used is recorded in the 'DL_mean_timestamp' field, but the overall temporal coverage is not explicitly stated.

Freshness

Last updated 2026-03-02 17:27:55.556299; freshness should be verified.

Geography

San Marino.