Vanuatu GIS data

geoBoundaries ADM2 boundaries for Vanuatu: 65 units, representing 2019. Source: Secretarial of the Pacific Community (SPC), Statistics for Development Division, OCHA ROP.

geoBoundaries ADM0 boundaries for Vanuatu: 1 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

geoBoundaries ADM1 boundaries for Vanuatu: 6 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

Open and free data for assessing the human presence on the planet. The Global Human Settlement Layer (GHSL) project produces global spatial information, evidence-based analytics, and knowledge describing the human presence on the planet. The GHSL relies on the design and implementation of spatial data processing technologies that allow automatic data analytics and information extraction from large amounts of heterogeneous geospatial data including global, fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources. The JRC, together with the Directorate-General for Regional and Urban Policy (DG REGIO) and Directorate-General for Defence Industry and Space (DG DEFIS) are working towards a regular and operational monitoring of global built-up and population based on the processing of Sentinel Earth Observation data produced by European Copernicus space program. In addition, the EU Agency for the Space Programme (EUSPA) undertakes activities related to user uptake of data, information and services.

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Vanuatu: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

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 Vanuatu **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0049 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0004 million km (7.4681%) - Unpaved roads: 0.0012 million km (24.7891%) About 0.0033 million km (67.7428%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0 million km (0.872%) 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

Population Raster Vanuatu 2020 Data Input: Household locations from 2016 Population and Housing Census conducted using Survey Solutions CAPI (Computer Assisted Personal Interview). Year Population Growth Rate of 2.3% has been applied to update population up to 2020 Household locations vector layer has been transformed into a 100m resolution raster. _Access this dataset from the Pacific Data Hub_

Spatial datasets utilized to conduct the spatial analysis and additional information from the research article: Coastal proximity of populations in 22 Pacific Island Countries and Territories. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223249 https://sdd.spc.int/mapping-coastal _Access this dataset from the Pacific Data Hub_

This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long

# EOS-RS Damage Proxy Map: Efate, Vanuatu, Earthquakes, 17 Dec 2024, v0.8 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Damage Proxy Map (DPM) depicting areas that are likely damaged in Efate, Vanuatu due to a powerful Mw7.3 earthquake and several strong aftershocks on 17 Dec 2024. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (19 Aug 2024 to 5 Dec 2024) and after (17 Dec 2024) the event. The map covers an area indicated by the white polygon. Damage is shown by colored pixels of 30m in size, where yellow to red indicates increasingly significant ground surface change before and during the event. Preliminary validation was conducted using news reports and ground-level imagery and videos in selected areas. This map should be used as a guidance to identify damaged areas, and may be less reliable over vegetated or mountainous areas. Scattered pixels over vegetated or mountainous areas may be false positives, and a lack of colored pixels over such areas may not mean no damage. The product contains modified Copernicus Sentinel data (2024), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS). **More map details and files:** **More map details and files:** **Legal Disclaimer:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2024)

# Vanuatu Cyclone Judy Cyclone Kevin: Damage Proxy Maps, Mar 2023 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage proxy maps for Vanuatu Cyclone Judy Cyclone Kevin (Mar 2023) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **EOS-RS Damage Proxy Map: Vanuatu, Cyclone Judy and Cyclone Kevin, 10 Mar 2023, v0.8** - **EOS-RS Damage Proxy Map: Vanuatu, Cyclone Judy and Cyclone Kevin, 10 Mar 2023, v0.8** - **EOS-RS Damage Proxy Map: Vanuatu, Cyclone Judy and Cyclone Kevin, 3 Mar 2023, v0.7** - **EOS-RS Damage Proxy Map: Vanuatu, Cyclone Judy and Cyclone Kevin, 3 Mar 2023, v0.7** **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Contains modified Copernicus Sentinel data (2022-2023)

# Vanuatu Cyclone Harold: Damage Proxy Maps, Apr 2020 The Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and Caltech, in collaboration with the Earth Observatory of Singapore (EOS), produced these damage proxy maps for Vanuatu Cyclone Harold (Apr 2020) from synthetic aperture radar (SAR) satellite images. Each map is a separate ZIP below with its GeoTIFF, a KMZ for Google Earth, the PNG figures and a text file describing how it was made and how to read it. ## Maps in this dataset - **ARIA-SG Damage Proxy Map: Vanuatu, Cyclone Harold, 10 Apr 2020, v0.1** - **ARIA-SG Damage Proxy Map: Vanuatu, Cyclone Harold, 10 Apr 2020, v0.1** **More map details and files:**

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3. Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

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

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

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

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

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

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

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

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

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

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

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

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).

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.

**UNOSAT code TC20230227VUT, GDACS Id: 1000965** This map illustrates damaged and potentially damaged structures and buildings in Luganville and South East Santo Councils of Vanuatu as detected using Pleiades satellite images acquired on 04 & 06 March 2023 after the passage of Kevin and Judy tropical cyclones. Within the analysed area UNOSAT identified 46 damaged and/or potentially damaged structures. 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 TC20230227VUT, GDACS Id: 1000965** This map illustrates damaged and potentially damaged structures and buildings in Tanna Island, Tafea Province of Vanuatu as detected using WorldView-2 satellite image acquired on 04 March 2023 after the passage of Kevin and Judy tropical cyclones. Within the analysed area UNOSAT identified 57 damaged and 88 potentially damaged structures. 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 TC20230227VUT, GDACS Id: 1000965** Status: Landslide, potentially affected road and damaged houses observed. Further action(s): Continue monitoring Summary of findings; Based on satellite images acquired on 04th, 09th and 11th of March 2023, the findings are the following:  Tanna Island, Tafea Province; Widespread affected forest observed over Tanna Island; Potentially affected houses observed in West Tanna  Council, Tafea Province; Widespread damaged houses observed in Lawir Village, Tanna Island, North Tanna Council, Tafea Province; Efate Island, Shefa Province; Widepsread affected forest observed over Efate Island; Tongoa Island, Shefa Province Widespread affected forest observed over Tongoa Island; South Pentecost Island, Penama Province No visible affected forest observed over cloud free areas in South Pentecost Island, South Pentecost Council, Penama Province; Ambae Island, West Ambae Council, Penama Province; Landslide observed in Ambae Island, West Ambae Council, Penama Province ;   Affected shoreline observed in Ambae Island, West Ambae Council, Penama Province ; Potentially affected road and potentially affected houses observed in Ambae Island, West Ambae Council, Penama Province ; Malekula Island, Malampa Province; Affected shoreline observed in South Malekula Council, Malampa Province;

**UNOSAT code TC20230227VUT, GDACS Id: 1000965** This map illustrates damaged and potentially damaged structures and buildings in Aniwa Island, Tafea Province of Vanuatu as detected using Pleiades satellite image acquired on 04 March 2023 after the passage of Kevin and Judy tropical cyclones. Within the analysed area UNOSAT identified 38 damaged and 67 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to 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: VO20211025VUT** This map illustrates the volcanic ashfall dispersion as observed using Sentinel-2 imagery acquired on 23 October 2021, 10:00 hrs. local time over Yasur volcano on Tanna Island, Vanuatu. At the time of the acquisition, the ashfall covered about 20 km2 at the Southwestern direction of the volcanic crater. Using the building footprints of Humanitarian Open Street Map as a baseline, around 440 buildings in the cloud free zone are located within areas covered by ash and smoke, representing 3% of the total number of buildings in Tanna island. 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: TC20200403VUT** This map illustrates potentially damaged structures and buildings in West Malo and East Malo Municipality, Sanma Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis used a Pleiades image acquired on 7 and 10 April 2020 as post event images, Within the Island extent, UNITAR-UNOSAT identified in the cloud free zones 1,345 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 45 % of the total number of structures within the analyzed cloud-free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: TC20200403VUT** This map illustrates potentially damaged structures and buildings in Ambae Island, Penama Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis used a WorldView-2 image acquired on 20 April 2020 and Pleiades image acquired on 21 April 2020 as post event images, Within the Island extent, UNITAR-UNOSAT identified in the cloud free zones about 60 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 1% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: TC20200403VUT** This map illustrates potentially damaged structures and buildings in Paama Island, Malampa Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis used a Pleiades image acquired on 21 April 2020 as post event images, Within the island boundary, UNITAR-UNOSAT identified in the cloud free zones about 100 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 30% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.