Road Surface Classification for Chad Using AI and OpenStreetMap

A geospatial dataset from HeiGIT details road surfaces in Chad, distinguishing paved from unpaved roads. It covers approximately 0.2286 million kilometers of roads, with AI-derived estimates classifying 3.3918% as paved and 46.3487% as unpaved. The data, last updated in March 2026, combines OpenStreetMap features with deep learning predictions from Mapillary imagery and urban classification layers.

Use Cases

• Transportation planning based on road surface type and length estimates.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment leveraging the distinction between paved and unpaved road networks.
• GIS applications for routing and logistics based on urban/rural classification and road attributes.

Strengths

• Includes AI-derived surface classifications for approximately 0.2286 million kilometers of roads.
• Combines multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides specific statistics on paved (0.0078 million km) and unpaved (0.1059 million km) road coverage.

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 50.2595% 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 and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

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Freshness

Last updated 2026-03 02 17:30:35.252609; freshness should be verified.

Geography

Chad