Cabo Verde Road Surface Classification from OpenStreetMap and Deep Learning

0.01 million km of roads in Cabo Verde are mapped, with AI-derived estimates classifying 0.0014 million km as paved and 0.0014 million km 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 gap where 0.0072 million km of road surface information is missing from OSM.

Use Cases

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

Strengths

• Provides AI-derived surface classifications for approximately 0.01 million km of roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban classification layers.
• Includes detailed attributes like highway type, surface, smoothness, and urban/rural classification.

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 GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

Mean timestamp of predictions is included, but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:24:21.817333; freshness should be verified.

Geography

Cabo Verde.