Kiribati Road Surface Classification from OpenStreetMap and AI

A geospatial dataset detailing road surfaces in Kiribati, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with AI-derived surface classifications from Mapillary imagery and urban layers. It was last updated on March 2, 2026, by the Heidelberg Institute for Geoinformation Technology (HeiGIT).

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the distinction between paved and unpaved road networks.
• GIS applications utilizing the geospatial format and urban/rural classification attributes.

Strengths

• Provides AI-derived surface classifications for roads where OpenStreetMap data is missing.
• Includes detailed OSM attributes such as name, highway type, surface, and width.
• Integrates urban classification from GHSU and AFRICAPOLIS layers for contextual analysis.

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.
• Approximately 54.84% of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

OpenStreetMap, Mapillary imagery, GHSU Global Human Settlement Urban Layer 2019, AFRICAPOLIS 2020.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

Mean timestamp of predictions is provided in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:12:35.075729; freshness should be verified.

Geography

Kiribati.