This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between **paved** and **unpaved** surfaces across the region.
This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the [paper](http://arxiv.org/abs/2410.19874)
Roughly 0.0006 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0001 and 0.0001 (in million kms), corressponding to 15.6158% and 21.2478% respectively of the total road length in the dataset region.
0.0004 million km or 63.1365% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0 million km of information (corressponding to 0.0949% of total missing information on road surface)
It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.
This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata.
This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.
AI features:
pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."
pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).
osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."
combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."
combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."
n_of_predictions_used: Number of predictions used for the feature length estimation.
predicted_length: Predicted length based on the DL model’s estimations, in meters.
DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.
OSM features may have these attributes(Learn what tags mean here):
name: Name of the feature, if available in OSM.
name:en: Name of the feature in English, if available in OSM.
name:* (in local language): Name of the feature in the local official language, where available.
highway: Road classification based on OSM tags (e.g., residential, motorway, footway).
surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).
smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).
width: Width of the road, where available.
lanes: Number of lanes on the road.
oneway: Indicates if the road is one-way (yes or no).
bridge: Specifies if the feature is a bridge (yes or no).
layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).
source: Source of the data, indicating the origin or authority of specific attributes.
Urban classification features may have these attributes:
continent: The continent where the data point is located (e.g., Europe, Asia).
country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).
urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)
urban_area: Name of the urban area or city where the data point is located.
osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.
osm_type: Type of OSM element (e.g., node, way, relation).
The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.
This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.
We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at
communications@heigit.org – we would be happy to amplify your work.
