Haiti Road Surface Classification from OpenStreetMap and AI Predictions

A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides AI-derived road surface classifications for Haiti. It covers approximately 52.5 thousand kilometers of roads from OpenStreetMap, distinguishing paved and unpaved surfaces using a hybrid deep learning approach. The data is augmented with urban classification layers and Mapillary imagery to address gaps in OSM surface tags.

Use Cases

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

Strengths

• Provides AI-derived surface estimates for approximately 52.5 thousand kilometers of roads.
• Includes multiple classification schemes (OSM-priority, DL-priority) for flexibility.
• Augments OSM data with deep learning predictions and urban layers to address data gaps.

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.
• About 73.45% of road surface information is reported as missing in the underlying OSM data.

Provenance

Source

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

Collection Method

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

Time Range

The mean timestamp of predictions used is included (DL_mean_timestamp), but the overall temporal coverage is not explicitly stated.

Freshness

Last updated 2026-03 02 17:23:28.537798; freshness should be verified.

Geography

Haiti.