Road Surface Classification for Niger from OpenStreetMap and AI

Niger's road network, comprising approximately 296,700 kilometers of mapped roads, is classified by surface type using a hybrid deep learning approach. The dataset, created by HeiGIT and last updated in March 2026, distinguishes paved from unpaved roads and integrates OpenStreetMap data with predictions from Mapillary imagery. It includes AI-derived features like predicted class and length, along with standard OSM attributes.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis using road attributes like surface, smoothness, and width.
• Climate emissions assessment leveraging road surface data for vehicle emission modeling.
• GIS applications for routing and network analysis in urban and rural areas.
• Validating and augmenting OpenStreetMap data with AI-derived surface predictions.

Strengths

• Covers approximately 296,700 kilometers of mapped roads in Niger.
• Provides AI-derived estimates for 6.212% paved and 63.4962% unpaved roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban classification layers.

Limitations

• Approximately 30.2918% of road surface information is missing in the underlying OSM data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

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 17:31:14.988797; freshness should be verified.

Geography

Niger