Nepal GIS data

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.

satellite imagery by Planet, acquired 2026-08-25 to 2026-08-25, 300 cm per pixel, published on OpenAerialMap.

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

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 Province 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 District

**UNOSAT code: FL20210630NPL** This map illustrates satellite-detected surface waters in Sudurpaschim and Lumbini provinces, Nepal as observed from a Sentinel-1 image acquired on 4 July 2021 at 18:38 local time and using an automated analysis with Machine learning method. Within the analyzed area of 7,055 km2, about 170 km2 of lands appear to be flooded. The water extent appears to have increased of about 150 km2 since 30 June 2021. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Kailali with ~28,000 people, Banke with ~23,000 people, and Bardiya with ~21,000 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20210630NPL** This map illustrates satellite-detected surface waters in Province 1, Nepal as observed from a Sentinel-1 image acquired on 4 July 2021 at 05:48 local time and using an automated analysis with Machine learning method. Within the analyzed area of 4,060 km2, about 240 km2 of lands appear to be flooded. The water extent appears to have decreased of about 200 km2 since 1 July 2021 and moved toward India. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Sunsari with ~186,000 people, Morang with ~83,600 people, and Jhapa with ~21,700 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20210630NPL** This map illustrates satellite-detected surface waters in Province 2, Lumbini, Gandaki,and Bagmati provinces , Nepal as observed from a Sentinel-1 image acquired on 3 July 2021 at 01:37 local time and using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 17,000 km2 and, about 830km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Parsa with ~386,000 people, Rautahat with ~221,500 people, Rupandehi with~148,800, and Bara with ~148,200 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

Nepal interpolated climate data produced by WorldClim for April and May 2015 http://www.worldclim.org/

This assessment of the radio stations in the areas affected by the Nepal earthquake includes information on station name, location, operating status, damage, and contact information.

Road network data for Nepal

Educational facilities and critical educational sector infrastructure in the Kathmandu Valley in Nepal. Data downloaded from the Open Cities Project Educational Facilities page.

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

Nepal administrative level 0-3 edge-matched gazetteer and boundary files. COD-EM datasets do not replace the authoritative COD-AB available here; however COD-EM datasets may be preferred for cartographic purposes. See caveats. These layers are suitable for database or GIS linkage to the Mauritania - Subnational Population Statistics tables using the ADM0, ADM1, ADM2, and ADM3_PCODE fields. Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

**UNOSAT code FL20221117NPL** This map illustrates satellite-detected surface waters in Banke District, Lumbini Province, Nepal as observed from Sentinel-2 images acquired on 25 & 30 October 2022 (Cumulative). Within the analyzed area of about 4,000 km², a combined 33 km² of lands appear to be flooded on the 25th and the 30th of October 2022. Water extent appears to have decreased of about 26 km² since the 15th October 2022. Based on Worldpop population data and the detected surface waters, about 1,400 people are potentially exposed or living close to flooded areas, of which 1,400 are located in Duduwa and Narainapur Palikas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code FL20221117NPL** This map illustrates satellite-detected surface waters in Sudurpaschim and Lumbini Provinces, Nepal as observed from Sentinel-2 images acquired on 25 & 30 October 2022 (Cumulative). Within the analyzed area of about 5,600 km², a combined 52 km² of lands appear to be flooded on the 25th and the 30th of October 2022. Water extent appears to have decreased of about 37 km² since the 15th October 2022. Based on Worldpop population data and the detected surface waters, about 23,000 people are potentially exposed or living close to flooded areas, of which 10,000 are located In Janaki, Tikapur, Geruwa, Rajapur, Thakurbaba & Madhuwan Palikas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code FL20221117NPL** This map illustrates satellite-detected surface waters in Sudurpaschim and Lumbini Provinces, Nepal as observed from a Sentinel-2 image acquired on 15 October 2022. Within the total analyzed area of about 4,700 km², ~70 km² of lands appear to be flooded. Based on Worldpop population data and the detected surface waters about 35,000 people are potentially exposed or living close to flooded areas, of which 18,000 are located In Janaki, Tikapur, Geruwa, Rajapur, Thakurbaba & Madhuwan Palikas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

**UNOSAT code: FL20200708NPL** This map illustrates satellite-detected surface waters in Province 2 of Nepal as observed from a Radarsat-2 image acquired on 14 July 2020 due to the current monsoon rains. Within the analyzed area of about 2,000 km2, a total of about 50 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20200708NPL** This map illustrates satellite-detected surface waters in Province 1 and 2 of Nepal as observed from a Radarsat-2 image acquired on 12 July 2020 due to the current monsoon rains. Within the analyzed area of about 3,000 km2, a total of about 475 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20200708NPL** This map illustrates satellite-detected surface waters in Province 1 and 2 of Nepal as observed from a Sentinel-1 image acquired on 6 July 2020. Within the analyzed area of about 2,700 km2, a total of a bout 76 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important Note: Flood analysis from Sentinel-1 imagery acquired on 6 July 2020 may seriously underestimate presence of standing flood water in built up areas due to backscattering properties

**UNOSAT code: FL20200708NPL** This map illustrates satellite-detected surface waters in Province 2 of Nepal as observed from a Sentinel-1 image acquired on 13 July 2020 due to the current monsoon rains. Within the analyzed area of about 2,300 km2, a total of about 200 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20200708NPL** This map illustrates satellite-detected surface waters in Province 2 of Nepal as observed from a TerraSAR-X image acquired on 12 July 2020 due to the current monsoon rains. Within the analyzed area of about 900 km2, a total of about 35 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: LS20240818NPL** This map illustrates satellite-detected landslides in Thame Village, Solukhumbu District, Koshi Province, Nepal, as observed from a PlanetScope image acquired on 17 August 2024 at 09:58 local time (04:13 UTC). Within the analysed area, about 140 hectares of landslide scars are observed. UNITAR-UNOSAT identified 43 Potentially damaged structures (20 % from the total buildings in the analysed area) and about 2 km of affected roads and paths. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240928NPL** This map illustrates the floods using Sentinel-1 images acquired on 27 September 2024 at 05:56 local time (00:11 UTC). Within the analysed area of about 7,000 km², a total of about 50 km² of land appears to be affected by flood waters. Based on Worldpop spatial demographic data, about 19,000 people are exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20240928NPL** This map illustrates the floods using Sentinel-1 images acquired on 29 September 2024 at 18:15 local time (12:30 UTC). Within the analysed area of about 20,000 km², a total of about 110 km² of land appears to be affected by flood waters. Based on Worldpop spatial demographic data, about 58,000 people are exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20240928NPL** This map illustrates the floods using the Spot-6 image acquired on 30 September 2024 at 14:20 local time (08:35 UTC). Within the analysed area of about 450 km², a total of about 8 km² of land appears to be affected by flood waters. and about 4 km² of cropland seems to be inundated. Based on Worldpop spatial demographic data, about 46,000 people are exposed or living close to flooded areas. UNITAR-UNOSAT identified about 5,000 potentially affected structures within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20240928NPL** This map illustrates the floods using Sentinel-1 images acquired on 01 October 2024 at 17:59 local time (12:14 UTC). Within the analysed area of about 6,000 km², a total of about 55 km² of land appears to be affected by flood waters. Based on Worldpop spatial demographic data, about 18,000 people are exposed or living close to flooded areas. Water extent seems to have increased by about 14 km² since 27 September 2024 This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

**UNOSAT code: FL20240928NPL** This map illustrates the floods using Sentinel-2 images acquired on 01 & 06 October 2024 at 10:42 local time (04:57 UTC). Within the analysed area of about 18,000 km², a total of about 300 km² of land appears to be affected by flood waters. Based on Worldpop spatial demographic data, about 115,000 people are exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20210630NPL** This map illustrates satellite-detected flash floods and landslides in Melamchi village, Melamchi Municipality, Bagmati province, Nepal as observed using Sentinel-2 satellite imagery acquired on 24 June 2021. Within the analyzed area, approximately 150 structures appear to be potentially affected by the landslides. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20210630NPL** This map illustrates satellite-detected surface waters in Nawalparasi West, Rupandehi and Kapilbastu districts, Lumbini province, Nepal as observed from a Sentinel-1 image acquired on 7 July 2021 at 06:12 local time and using an automated analysis with Machine learning method. Within the analyzed area of about 1,500 km2 and, about 67km2 of lands appear to be flooded. The water extent appears to have decreased of about 110 km2 since 2 July 2021 and moved toward India. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Rupandehi with ~59,000 people and Nawalparasi West with ~5,200 people. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**UNOSAT code: FL20210630NPL** This map illustrates satellite-detected surface waters in Province 1 and 2 as observed from a Sentinel-1 image acquired on 1 July 2021 at 05:58 local time and using an automated analysis with Artificial Intelligence based methods. Within the analyzed area of about 110,000 km2 and, about 645km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, about 700,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

**ADAM ID: **sm\_us6000pvwf Magnitude 5.5 earthquake at 10.0 depth occurred on Feb 27 2025 9km NE of Ratmate. The epicentre of the earthquake was located at latitude 27.8265 longitude 85.9676. It impacted 289469 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

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

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