Road Surface Data [Nauru] Full Details

Full Details

Title:

Road Surface Data [Nauru]

Description:

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 0.0001 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 0.0 and 0.0 (in million kms), corressponding to 23.1658% and 23.0738% respectively of the total road length in the dataset region. 0.0001 million km or 53.7604% 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 2.5123% 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](https://wiki.openstreetmap.org/wiki/Tags)): - **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](https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology). See the [HeiGIT](https://heigit.org/) 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](mailto:communications@heigit.org) – we would be happy to amplify your work.

Provider:

Humanitarian Data Exchange

Resource Class:

Datasets

Temporal Coverage:

2024

Place:

Nauru

Access Rights:

Public

Format:

Files

Language:

English

Date Added:

2024-12-31