Thailand Road Surface Classification from OpenStreetMap and AI

HeiGIT's dataset provides detailed road surface information for Thailand, classifying roads as paved or unpaved. It covers approximately 1.086 million kilometers of roads from OpenStreetMap, with AI-derived estimates filling gaps in the original data. The dataset was last updated on March 2, 2026.

Use Cases

• Transportation planning based on road surface type and length statistics.
• 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 data and urban classification attributes.

Strengths

• Covers approximately 1.086 million kilometers of roads in Thailand.
• Provides AI-derived estimates for 0.1802 million km of paved and 0.2861 million km of unpaved roads.
• Augments OpenStreetMap data with deep learning predictions from Mapillary imagery and urban layers.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Approximately 0.6197 million km (57.0604%) of road surface information is missing in the original OSM data.

Provenance

Source

OpenStreetMap (OSM), augmented with deep learning predictions from Mapillary imagery and GHSU/AFRICAPOLIS urban layers.

Collection Method

Surface classification derived using a hybrid deep learning approach, with methodology detailed in linked papers.

Time Range

Mean timestamp of predictions (DL_mean_timestamp) is provided per feature.

Freshness

Last updated 2026-03 02 17:09:33.276597; freshness should be verified.

Geography

Thailand.