Maldives Road Surface Classification from OpenStreetMap and AI

0.0022 million kilometers of roads from OpenStreetMap are classified as paved or unpaved using a hybrid deep learning approach. The dataset, created by HeiGIT, was last updated on March 2, 2026, and integrates Mapillary imagery with urban layers from GHSU and AFRICAPOLIS. It estimates that 9.0246% of mapped roads are paved and 12.811% are unpaved, while 78.1644% of surface information is missing in OSM.

Use Cases

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

Strengths

• Includes AI-derived surface classifications (paved/unpaved) for roads.
• Integrates multiple data sources: OpenStreetMap, Mapillary imagery, GHSU, and AFRICAPOLIS urban layers.
• Provides specific statistics on road length and surface coverage (e.g., 78.1644% of surface info is missing in OSM).

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.
• Row count and file size are unknown, which may limit suitability assessment.

Provenance

Source

HeiGIT (Heidelberg Institute for Geoinformation Technology)

Collection Method

Data originates from OpenStreetMap, augmented with deep learning predictions from Mapillary imagery and combined with urban classification layers.

Time Range

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Freshness

Last updated 2026-03-02 17:16:19.928990; freshness should be verified.

Geography

Maldives