Peru Road Surface Classification from OpenStreetMap and Deep Learning

A geospatial dataset provides detailed information on road surfaces across Peru, distinguishing between paved and unpaved roads. It contains approximately 0.4981 million kilometers of roads mapped in OpenStreetMap, with surface classifications derived using a hybrid deep learning approach based on Mapillary imagery. The dataset was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• Infrastructure analysis based on attributes like highway type, surface, and smoothness.
• Climate emissions assessment based on the proportion of paved versus unpaved roads.
• GIS applications for routing and logistics based on urban/rural classification and road network data.

Strengths

• Contains approximately 0.4981 million kilometers of mapped roads.
• Provides AI-derived estimates for 0.0621 million km of paved and 0.1696 million km of unpaved roads.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery and urban classification layers.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• Approximately 0.2665 million km (53.4929%) of road surface information is 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.

Time Range

null

Freshness

Last updated 2026-03-02 17:22:10.539529; freshness should be verified.

Geography

Peru