Malawi Road Surface Classification from OpenStreetMap and AI Predictions

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Malawi. It covers approximately 221,600 kilometers of OpenStreetMap roads, classifying them as paved or unpaved using a hybrid deep learning model. The data is augmented with predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using predicted road lengths and surface types.
• Climate emissions assessment based on road surface material data.
• GIS applications leveraging geospatial road attributes and urban/rural classifications.

Strengths

• Includes AI-derived predictions for road surfaces, addressing a 77.27% data gap in original OSM tags.
• Combines data from multiple sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.
• Provides specific statistics, such as 6,900 km of paved roads and 43,500 km of unpaved roads.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Data may reflect geographic or source bias inherent to the underlying OpenStreetMap and imagery data.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

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

Time Range

null

Freshness

Last updated 2026-03 02.

Geography

Malawi