Togo Road Surface Classification from OpenStreetMap and AI

A 2026 dataset from HeiGIT provides AI-derived road surface classifications for Togo, distinguishing paved and unpaved roads. It covers approximately 90,600 km of roads mapped in OpenStreetMap, with 6.3% paved and 38.4% unpaved according to model predictions. The data is augmented with deep learning predictions from Mapillary imagery and urban layers from GHSU and AFRICAPOLIS.

Use Cases

• Transportation planning based on road surface type (paved/unpaved) and highway classification.
• Infrastructure analysis using attributes like surface, smoothness, width, and bridge status.
• Climate emissions assessment leveraging road surface and urban/rural classification data.
• GIS applications utilizing geospatial road network data with AI-predicted surface classes.

Strengths

• Includes AI-derived surface predictions for approximately 90,600 km of roads.
• Combines OpenStreetMap data with deep learning predictions and urban classification layers.
• Provides multiple classification fields (OSM, DL, combined) for comparison and validation.

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 55.3% of road surface information is reported as missing in the base OSM data.

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 referenced arXiv papers.

Time Range

The mean timestamp of predictions used is recorded in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:08:52.061089; freshness should be verified.

Geography

Togo.