Roads of Russian Federation

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between paved and unpaved roads across the region. Surface information is derived using a hybrid deep learning approach. For methodological details, see: http://arxiv.org/abs/2410.19874 Recently, we also published a new country-level dataset based on PlanetScope satellite imagery with near-complete coverage of all arterial roads. It is available on HDX under the name: "Russian Federation: **Planet** Road Surface". The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 29.7563 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 10.0288 million km (33.7032%) - Unpaved roads: 4.8133 million km (16.1756%) About 15.3793 million km (51.6843%) of roads lack surface information in OSM. To reduce this gap, the Mapillary-derived surface dataset provides an additional 1.3318 million km (8.66%) of surface information. Intended Use This dataset supports transportation planning, infrastructure assessment, climate emissions analysis, and GIS applications. AI Features - pred_class: Model-predicted surface class ("paved" or "unpaved") - pred_label: Binary label (0 = paved, 1 = unpaved) - osm_surface_class: Surface classification derived from OSM tags - combined_surface_osm_priority: Combined classification prioritizing OSM - combined_surface_DL_priority: Combined classification prioritizing DL prediction - n_of_predictions_used: Number of predictions used for estimation - predicted_length: DL-estimated length (meters) - DL_mean_timestamp: Mean timestamp of predictions OSM Attributes - name - name:en - name:* (local language) - highway - surface - smoothness - width - lanes - oneway - bridge - layer - source Urban Classification Attributes - continent - country_iso_a2 - urban (0 = rural, 1 = urban) - urban_area - osm_id - osm_type Data Sources The dataset originates from OpenStreetMap (OSM) and is augmented with deep learning predictions based on Mapillary street-level imagery, combined with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS 2020 urban layer. More HeiGIT datasets: https://data.humdata.org/organization/heidelberg-institute-for-geoinformation-technology More information: https://heigit.org/ Contact: communications@heigit.org

Railway lines and stations tagged `railway=*`: rail lines, tram tracks, and stations. Useful for transport accessibility analysis and basemap context. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: railway - Part of the country exports on HDX by HOT

Airports and aviation infrastructure: airports, heliports, helipads, runways, terminals, and emergency landing sites tagged via `aeroway`, `building`, or `emergency`. Useful for logistics, evacuation routing, and aerial-response planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: aeroway, building, emergency - Part of the country exports on HDX by HOT

Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT

A city GIS-project in tandem with Bluewater Trails to determine cycling comfortability scores throughout the city. Based on weighted criteria related to road infrastructure characteristics as well as traffic context. The city does not guarantee the complete accuracy of this data.

Linear representation of transit priority measures such as transit priority lanes, in lane stops, and separated right-of-way.Transit Priority Corridors are streets that have been strategically chosen to include measures to improve the efficiency of buses along the entire corridor. Metadata