Nepal transport data

Road network across the area tagged `highway=*`: motorways, residential streets, service roads, footways, and trails. Useful for routing, accessibility analysis, and evacuation 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: highway - 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

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

Accessibility to cities dataset is modeled as raster-based travel time/cost analysis. The model travel time/cost from/to cities is defined as the OpenStreetMap national capital, cities, and towns within Nepal. This 1km resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location) **Data publication:** 2021-10-15 **Contact points:** Metadata Contact: [FAO-DATA](mailto:fao-data@fao.org) Resource Contact: [Andry Rajaoberison](mailto:Andry.Rajaoberison@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, waterways, Global Multi-resolution Terrain Elevation Data 30s (GMTED2010), The Hand-in-Hand Initiative, 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to major cities (Nepal ~ 1Km)

Accessibility to regional cities dataset is a raster-based analysis to calculate the travel time/cost to regional cities. Regional cities are defined as OpenStreetMap cities within a 500km radius, within the Nepal border. This 1km resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location). **Data publication:** 2021-10-13 **Contact points:** Metadata Contact: [FAO-DATA](mailto:fao-data@fao.org) Resource Contact: [Andry Rajaoberison](mailto:Andry.Rajaoberison@fao.org) **Data lineage:** lineage statement Produced using OpenStreetMap data for roads, railways, waterways, Global Multi-resolution Terrain Elevation Data 30s (GMTED2010), ESRI World Cities, The Hand-in-Hand Initiative, 2020 **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped raster TIF file for accessibility to regional cities (Nepal ~ 1Km)

Line features representing transportation routes in Nepal, including local trails, with attributes such as length and feature codes. Each feature represents a segment of a route. Published by Orion Terrametrics.

Road network data for Nepal

Data shows the location of airports and Humanitarian Staging Area (HSA) locations in Nepal.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM), distinguishing between **paved** and **unpaved** roads across the region. Surface classification 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 Nepal **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.29 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0075 million km (2.5859%) - Unpaved roads: 0.0495 million km (17.0515%) About 0.2331 million km (80.3627%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0006 million km (0.2446%) of the missing surface information. Intended Use This dataset supports transportation planning, infrastructure analysis, climate emissions assessment, 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**: Predicted length based on DL estimation (meters) - **DL_mean_timestamp**: Mean timestamp of predictions used 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

We have assessed *primary and secondary roads* for signs of road blockages in the *Rasuwa, Nuwakot and Kathmandu* regions in Nepal. **Brief Overview: 28/08/2026 Dataset** -We found 22 road blockage locations (19 of these locations were severely flooded) -The NH42 road was highly impacted by flooding, and parts of NH03, NH18 and F181 roads were affected by flooding. -The data is held in a point shapefile format in WGS 84 EPSG: 4326. *This dataset is derived from crowd sourcing. The next data release is planned for 11/09/2026.* You can find the Planetary Response Network: Nepal Flash Flooding 2026 project we ran here.