Tajikistan Road Surface Classification from OpenStreetMap and AI

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) providing road surface classifications for Tajikistan. It combines OpenStreetMap data with deep learning predictions derived from Mapillary street-level imagery, classifying roads as paved or unpaved. The dataset is intended for transportation planning, infrastructure analysis, and GIS applications.

Use Cases

• Transportation planning based on paved vs. unpaved road classifications.
• Infrastructure analysis using combined OSM and deep learning surface attributes.
• Climate emissions assessment based on road surface type and urban/rural classification.
• GIS applications leveraging geospatial road network data with surface quality indicators.

Strengths

• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban classification layers.
• Provides AI-derived surface classifications (paved/unpaved) to augment OSM data.
• Includes urban/rural classification via the GHSU and AFRICAPOLIS layers.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and total road length statistics are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Hybrid deep learning approach applied to OpenStreetMap and Mapillary street-level imagery, combined with urban classification layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:08:49.322727; freshness should be verified.

Geography

Tajikistan