Philippines Road Surface Classification from OSM and AI

Approximately 0.4963 million kilometers of Philippine roads are mapped, with AI-derived estimates classifying 16.4302% as paved and 10.3289% as unpaved. The dataset, created by HeiGIT and last updated in March 2026, combines OpenStreetMap data with deep learning predictions from Mapillary imagery to fill information gaps. It includes attributes for highway type, surface condition, and urban classification.

Use Cases

• Transportation planning based on road surface type and highway classification.
• Infrastructure analysis using attributes like surface, smoothness, and bridge status.
• Climate emissions assessment based on paved versus unpaved road networks.
• GIS applications for urban-rural classification and spatial analysis of road networks.

Strengths

• Covers approximately 0.4963 million kilometers of roads in the Philippines.
• Provides AI-derived surface estimates for over 26% of the mapped road network.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, and urban settlement layers.

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

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, with methodology detailed in linked arXiv papers.

Time Range

Mean prediction timestamps are included, but the overall temporal coverage of the base OSM data is not specified.

Freshness

Last updated 2026-03-02 17:18:36.769278; freshness should be verified.

Geography

The Philippines.