Montserrat Road Surface Classification from OpenStreetMap and Deep Learning

Approximately 0.3 km of roads are mapped for Montserrat, with 26.5% classified as paved and 10.7% as unpaved. The dataset is produced by HeiGIT using a hybrid deep learning approach on Mapillary imagery and OSM data, combined with urban layers. It was last updated on March 2, 2026.

Use Cases

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

Strengths

• Includes AI-derived surface classifications (paved/unpaved) for approximately 0.3 km of roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides multiple classification fields (OSM, DL-prioritized, combined) for comparison.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Approximately 62.8% of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and urban layers (GHSU, AFRICAPOLIS).

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

Mean timestamp of predictions is provided (DL_mean_timestamp), but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:18:43.928033; freshness should be verified.

Geography

Montserrat.