Madagascar Road Surface Classification from OSM and AI Predictions

Madagascar road network data from OpenStreetMap, enhanced with AI-derived surface classifications. The dataset covers approximately 0.5261 million kilometers of roads, with deep learning predictions distinguishing paved and unpaved surfaces. It was created by HeiGIT and last updated in March 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging the classification of paved versus unpaved roads.
• GIS applications utilizing the geospatial road network and urban classification attributes.

Strengths

• Includes AI-predicted surface classes for roads where OSM data is missing.
• Provides detailed statistics, including 0.0059 million km of paved roads and 0.0688 million km of unpaved roads.
• Combines data from multiple sources: OSM, Mapillary imagery, and urban settlement layers.

Limitations

• A significant portion (85.8189%) of road surface information is missing in the original OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and sample data are unavailable, which may limit suitability assessment.

Provenance

Source

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

Collection Method

Surface classification derived using a hybrid deep learning approach, as detailed in the referenced papers.

Time Range

null

Freshness

Last updated 2026-03-02 17:30:00.786706; freshness should be verified.

Geography

Madagascar