Marshall Islands Road Surface Classification from OSM and AI Predictions

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for the Marshall Islands. It combines OpenStreetMap (OSM) data with deep learning predictions from Mapillary imagery to distinguish paved and unpaved roads. The data is intended for transportation planning, infrastructure analysis, and climate emissions assessment.

Use Cases

• Transportation planning based on road surface type and predicted length.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications leveraging urban classification attributes like rural/urban designation.
• Data quality analysis comparing OSM-derived surface tags with AI predictions.

Strengths

• Integrates multiple data sources: OSM, Mapillary imagery, and urban settlement layers.
• Provides AI-derived predictions for road surface where OSM data is missing.
• Includes detailed OSM attributes like highway type, surface, and width.
• Offers combined classifications prioritizing either OSM tags or deep learning predictions.

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 74.4% of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery.

Time Range

null

Freshness

Last updated 2026-03 02 17:24:27.412633; freshness should be verified.

Geography

Marshall Islands