Nepal GIS data
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This dataset contains annual, 100m-resolution above ground biomass (AGB) estimates from CTrees, a NASA-affiliated forest-carbon monitoring organization, clipped to this country's boundary from CTrees' global AGB raster. Pixel values are stored as int16, scaled x10 relative to the true value in megagrams per hectare (Mg/ha) -- divide by 10 to get Mg/ha. See the caveats field and resource description for details on how this scale factor is recorded and why it isn't applied automatically by most tools.
Financial services: banks, ATMs, post offices, money transfer agents, and microfinance branches tagged via `amenity`. Useful for financial-inclusion mapping, cash-distribution planning, and economic analysis. 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: amenity - Part of the country exports on HDX by HOT
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
Settlements and residential areas: named cities, towns, villages, suburbs, and residential land (`place=*` and `landuse=residential`). Useful for demographic context, settlement-level targeting, and population analysis. 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: place, landuse - 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
Cultural and historic places: museums, galleries, theatres, places of worship, monuments, and historic sites tagged via `tourism`, `amenity`, `historic`, or `heritage`. Useful for heritage protection, cultural mapping, and tourism 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: tourism, amenity, historic, heritage - Part of the country exports on HDX by HOT
Points of interest: public-facing amenities, shops, tourist attractions, and landmarks tagged via `amenity`, `shop`, `tourism`, or `man_made`. Useful for general POI reference, urban analysis, and basemap detail. 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: amenity, shop, tourism, man_made - Part of the country exports on HDX by HOT
Health facilities: hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare`. Useful for service-access analysis, emergency response, and healthcare gap mapping. 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: amenity, healthcare - Part of the country exports on HDX by HOT
Water features across the area: rivers, streams, canals, lakes, ponds, and water bodies tagged with `waterway`, `water`, or `natural=water`. Useful for flood-risk modelling, watershed analysis, and environmental 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: waterway, water, natural - Part of the country exports on HDX by HOT
Education facilities: schools, kindergartens, colleges, and universities tagged via `amenity` or `building`. Useful for school-access mapping, education planning, and resource allocation. 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: amenity, building - Part of the country exports on HDX by HOT
Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban 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: building - 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
Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is a biophysical dimensionless quantity (its values range from 0/no absorption to 1/total absorption) used to assess the greenness and health of vegetation. FAPAR anomalies can be used as an indicator to detect and monitor the impacts of agricultural drought on the growth and productivity of vegetation. The data is presented in 10-day time composite of the Visible Infrared Imaging Radiometer Suite (VIIRS).
OpenStreetMap and Overture Maps vector layers for the area flooded in the 2026 Bhote Koshi and Trishuli flood in Nepal, and the ground immediately around it. The area of interest is the observed flood extent of 27 August 2026 widened by 200 metres. The extent itself was mapped from drone, Landsat, PlanetScope and Sentinel imagery and is published here as its own resource, so the water line can be separated from the margin around it. That margin brings in buildings and roads beside the water, so a feature being in this dataset is not evidence that it was flooded or damaged. Clip to the Flood Extent resource when you need figures for the water itself. The area follows the river from Rasuwagadhi to the Narayani confluence, across Bagmati and Gandaki Provinces and the districts of Rasuwa, Nuwakot, Dhading, Gorkha and Chitawan. Every layer holds what is standing and what has been lost, so buildings and roads do not vanish from a download once mappers record them as destroyed. `status` says Standing, Damaged or Destroyed, and the Destroyed and Damaged Features layer gathers everything marked destroyed or damaged in one place. Layers are rebuilt straight from OpenStreetMap through the day, so they carry volunteer work as it lands. Buildings, schools, health facilities and points of interest also come in an Overture version, which fuses Microsoft, Google and OpenStreetMap and reaches ground volunteers have not mapped yet. Overture follows its own release cycle and will not show mapping done during this response, so for damage work prefer the OpenStreetMap layers and use Overture where their coverage is thin. Mapping task boundaries from the Tasking Manager campaign show where volunteers are being asked to map. The bridge damage assessment is reported from the ground by volunteers and lists road bridges with their condition after the flood, deck length where measured, and administrative codes from the same boundaries as the other layers. For the wider 1 km river corridor under assessment, use Nepal Flood 2026 River Corridor. AI-scored building damage from post-event imagery is published separately as Nepal Flood 2026: fAIr Damage Assessment. Subscribe to the dataset for updates as mapping progresses. - Source: OpenStreetMap, Overture Maps, NAXA and volunteer field reports - Part of HOT disaster response 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)
The raster dataset consists of a 1km score grid for cereal storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 ) **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 Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Global Agro-Ecological Zones V4 (GAEZ) - Actual Yields and Production 2010 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for cereals location score (Nepal - ~ 1Km)
The raster dataset consists of a 1km score grid for fruits and nuts storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 ) **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 Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Global Agro-Ecological Zones V4 (GAEZ) - Actual Yields and Production 2010 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for fruits and nuts location score (Nepal - ~ 1Km)
The raster dataset consists of a 1km score grid for vegetables storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 ) **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 Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Global Agro-Ecological Zones V4 (GAEZ) - Actual Yields and Production 2010 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for Nepal vegetables location score.
The raster dataset consists of a 1km score grid for major cereals storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. Major cereals include barley, millet, wheat, maize, sorghum, and rice. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Crop. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Regional Cities Weighted Accessibility” *0.1 ) **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 Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Global Agro-Ecological Zones V4 (GAEZ) - Actual Yields and Production 2010 3. OpenStreetMap. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:** Zipped TIF raster file for major cereals location score (Nepal - ~ 1Km)
satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.
satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.
satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.
satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM3 boundaries for Nepal: 774 units, representing 2019. Source: Open Data Nepal, Ministry of Federal Affairs and General Administration.
Natural Earth admin 0 countries nep at 1:10 million scale: public domain cartographic vector data built to make small-scale world, regional and country maps.
This dataset captures educational points of interest (e.g., schools and universities) from the Overture Places theme, using Meta and Microsoft open data. Read more at https://docs.overturemaps.org/guides/places/
This theme includes all OpenStreetMap features in this area matching ( Learn what tags means [here](https://wiki.openstreetmap.org/wiki/Tags) ) : tags['amenity'] IN ('kindergarten', 'school', 'college', 'university') OR tags['building'] IN ('kindergarten', 'school', 'college', 'university') Features may have these attributes: - [name](http://wiki.openstreetmap.org/wiki/Key:name) - [name:en](http://wiki.openstreetmap.org/wiki/Key:name:en) - [amenity](http://wiki.openstreetmap.org/wiki/Key:amenity) - [building](http://wiki.openstreetmap.org/wiki/Key:building) - [operator:type](http://wiki.openstreetmap.org/wiki/Key:operator:type) - [capacity:persons](http://wiki.openstreetmap.org/wiki/Key:capacity:persons) - [addr:full](http://wiki.openstreetmap.org/wiki/Key:addr:full) - [addr:city](http://wiki.openstreetmap.org/wiki/Key:addr:city) - [source](http://wiki.openstreetmap.org/wiki/Key:source) - [name:ne](http://wiki.openstreetmap.org/wiki/Key:name:ne) This dataset is one of many [OpenStreetMap exports on HDX](https://data.humdata.org/organization/hot). See the [Humanitarian OpenStreetMap Team](http://hotosm.org/) website for more information.
satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Nepal Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Open Knowledge Nepal - localboundries.oknp.org. These were published in December 2022. Other administrative boundaries for this country are also available: Province District
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Nepal District Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Open Knowledge Nepal - localboundries.oknp.org. These were published in December 2022. Other administrative boundaries for this country are also available: Country Province
satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.
uav imagery by Rabi Shrestha, acquired 2026-07-05 to 2026-07-06, 2.2 cm per pixel, published on OpenAerialMap.
satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.