Nicaragua Road Surface Classification from OSM and Satellite Imagery

A geospatial dataset provides detailed information on road surfaces in Nicaragua, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban classification layers. It was last updated on 2026-03 02 by the Heidelberg Institute for Geoinformation Technology (HeiGIT).

Use Cases

• Transportation planning based on road surface classification.
• Infrastructure analysis based on paved and unpaved road lengths.
• Climate emissions assessment based on road network characteristics.
• GIS applications based on geospatial road attributes and urban classification.

Strengths

• Provides AI-derived estimates for approximately 0.066 million km of roads.
• Includes multiple classification schemes (OSM, DL-prioritized, OSM-prioritized).
• Augments OSM data with an additional 0.0004 million km of surface information from Mapillary.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• About 0.014 million km (21.2893%) of road surface information is missing in the underlying OSM data.

Provenance

Source

OpenStreetMap (OSM), Mapillary imagery, GHSU Global Human Settlement Urban Layer 2019, AFRICAPOLIS 2020.

Collection Method

Surface classification derived using a hybrid deep learning approach.

Time Range

Mean timestamp of predictions used is recorded in the 'DL_mean_timestamp' field.

Freshness

Last updated 2026-03-02 17:32:05.129412; freshness should be verified.

Geography

Nicaragua.