Suriname Road Surface Classification from OSM and Satellite Imagery

HeiGIT's dataset provides detailed road surface classifications for Suriname, distinguishing paved and unpaved roads. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and is augmented with urban classification layers. The dataset includes approximately 0.0182 million km of mapped roads, with AI-derived estimates for paved (0.0027 million km) and unpaved (0.0035 million km) segments.

Use Cases

• Transportation planning based on road surface type and length data.
• Infrastructure analysis using attributes like highway type, surface, and smoothness.
• Climate emissions assessment leveraging the distinction between paved and unpaved road networks.
• GIS applications utilizing the geospatial road network and urban classification attributes.

Strengths

• Integrates multiple data sources: OpenStreetMap, deep learning predictions from Mapillary, and urban layers (GHSU, AFRICAPOLIS).
• Provides AI-derived surface estimates for approximately 0.0182 million km of roads.
• Includes detailed OSM and urban classification attributes for each road segment.

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.

Provenance

Source

Heidelberg Institute for Geoinformation Technology (HeiGIT)

Collection Method

Derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary imagery, combined with urban classification layers.

Time Range

null

Freshness

Last updated 2026-03-02 17:01:16.430178; freshness should be verified.

Geography

Suriname