Guyana Road Surface Classification from OSM and Deep Learning

A dataset providing detailed information on road surfaces in Guyana, distinguishing between paved and unpaved roads. It contains approximately 0.0157 million kilometers of roads mapped in OpenStreetMap, with AI-derived estimates classifying 8.1637% as paved and 25.1713% as unpaved. The data was created by HeiGIT and last updated on March 2,我们发现了一个错误。

Use Cases

• Transportation planning based on road surface type and length data.
• Infrastructure analysis using attributes like highway type, surface, and width.
• Climate emissions assessment based on paved versus unpaved road classifications.
• GIS applications for routing and network analysis using the geospatial road data.

Strengths

• Includes AI-derived surface classifications for roads, with specific percentages for paved (8.1637%) and unpaved (25.1713%) types.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery to address data gaps.
• Contains multiple classification schemes (OSM-priority and DL-priority) and urban/rural indicators for analysis.

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.
• A significant portion (66.665%) of road surface information is reported as missing in the original 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

Mean timestamp of predictions (DL_mean_timestamp) is provided per feature, but overall temporal coverage is not specified.

Freshness

Last updated 2026-03-02 17:17:16.014092; freshness should be verified.

Geography

Guyana