Road Surface Classification for the State of Palestine from OSM and AI

HeiGIT provides a geospatial dataset classifying road surfaces as paved or unpaved across the State of Palestine. The data originates from OpenStreetMap and is augmented with deep learning predictions derived from Mapillary street-level imagery. The dataset was last updated on March 2, 2026.

Use Cases

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

Strengths

• Includes AI-derived surface classifications (paved/unpaved) from a hybrid deep learning approach.
• Combines data from multiple sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.
• Provides multiple classification fields, including OSM-derived, DL-prioritized, and combined versions.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Key statistics in the description show zero values (0.0 million km), suggesting placeholder figures.

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; methodology detailed in linked arXiv papers.

Time Range

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

Freshness

Last updated 2026-03-02 17:19:37.581426; freshness should be verified.

Geography

State of Palestine.