Road Surface Classification for Aruba from OpenStreetMap and Satellite Imagery

A geospatial dataset providing detailed road surface classifications for Aruba, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary street-level imagery and PlanetScope satellite data. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and urban/rural classification attributes.
• Infrastructure analysis using predicted surface class and OSM-derived attributes like highway type and width.
• Climate emissions assessment leveraging the distinction between paved and unpaved road lengths.
• GIS applications utilizing the geospatial geometry and combined surface classifications from OSM and deep learning.

Strengths

• Integrates multiple data sources: OpenStreetMap, Mapillary street-level imagery, and PlanetScope satellite data.
• Includes AI-derived predictions for road surface class with confidence metrics like 'n_of_predictions_used'.
• Provides urban/rural classification via integration with GHSU and AFRICAPOLIS urban layers.
• Offers combined classifications that prioritize either OSM tags or deep learning predictions.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT), based on OpenStreetMap, Mapillary, and PlanetScope.

Collection Method

Surface classification derived using a hybrid deep learning approach on street-level and satellite imagery, combined with OSM attributes.

Freshness

Last updated 2026-03-02 17:09:19.338030; freshness should be verified.

Geography

Aruba