Road Surface Classification for Sao Tome and Principe from OSM and Deep Learning

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for Sao Tome and Principe. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery, covering approximately 1.7 km of roads. The data distinguishes paved and unpaved surfaces and is intended for transportation and infrastructure analysis.

Use Cases

• Transportation planning based on paved versus unpaved road classifications.
• Infrastructure analysis based on road attributes like surface type, width, and lanes.
• Climate emissions assessment based on road surface data and urban/rural classification.
• GIS applications for humanitarian access and logistics based on road network data.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers from GHSU and AFRICAPOLIS.
• Provides AI-derived estimates for road surface types, quantifying 0.5 km as paved and 0.3 km as unpaved.
• Includes detailed OSM and urban classification attributes such as highway type, surface, smoothness, and urban/rural flag.

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 0.9 km (52.416%) 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 (OSM) and augmented with deep learning predictions based on Mapillary street-level imagery, combined with GHSU and AFRICAPOLIS urban layers.

Time Range

The dataset uses a 'DL_mean_timestamp' field, but the overall temporal coverage is not explicitly stated.

Freshness

Last updated 2026-03-02 17:08:59.174599; freshness should be verified.

Geography

Sao Tome and Principe