Guyana GIS data

geoBoundaries ADM2 boundaries for Guyana: 27 units, representing 2019. Source: DEVInfo LAC project, OCHA FISS.

geoBoundaries ADM0 boundaries for Guyana: 1 units, representing 2014. Source: geoBoundaries, Wikimedia Commons.

geoBoundaries ADM1 boundaries for Guyana: 10 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

uav imagery by horizonte topo, acquired 2026-07-18 to 2026-07-25, 21.7 cm per pixel, published on OpenAerialMap.

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.

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2. 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.

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 Guyana **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.0157 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.0013 million km (8.1637%) - Unpaved roads: 0.0039 million km (25.1713%) About 0.0105 million km (66.665%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0001 million km (0.4934%) 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

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

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.

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

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

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

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

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

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

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

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

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

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

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).

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. Guyana Country Boundary provides a 2021 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 2021 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Institut National de l'Information Géographique et Forestière 2016.. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Region

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. Guyana Region Boundaries provides a 2021 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 2021 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Institut National de l'Information Géographique et Forestière 2016.. These were published in October 2022. A new layer will be published in 18 months. Other administrative boundaries for this country are also available: Country

**UNOSAT code: FL20210520GUY** This map illustrates satellite-detected surface waters along the Mazaruni river in Cuyuni-Mazaruni Region of Guyana as observed from RCM-2 radar image acquired on 09 June 2021 at 22:17 UTC. Within the analyzed area of about 700 km2, a total of about 4 km2 of lands were observed as flooded. This is a preliminary analysis that 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.

Guyana administrative level 0-2 boundaries (COD-AB) dataset. The date that these administrative boundaries were established is unknown. NOTE: Administrative level 2 cannot be reconciled between the COD-AB and COD-PS. This COD-AB was most recently reviewed for accuracy and necessary changes in November 2024. The COD-AB does not require any update. Sourced from DEVInfo LAC project Vetting by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the Guyana COD-PS. No edge-matched (COD-EM) version of this COD-AB has yet been prepared. Please see the COD Portal. Administrative level 1 contains 10 feature(s). The normal administrative level 1 feature type is ""Region"". Administrative level 2 contains 27 feature(s). The normal administrative level 2 feature type is ""currently not known"". Recommended cartographic projection: South America Albers Equal Area Conic This metadata was last updated on January 9, 2025.

**UNOSAT code: FL20220526GUY** This map illustrates cumulative satellite-detected water using VIIRS in Guyana between 25 to 29 May 2022 compared with the period from 20 to 24 May 2022. Within the cloud free analyzed areas of about 205,000 km2, a total of about 3,900 km2 of lands appear to be affected with flood waters. Water extent appears to have increased of about 1,200 km2 since the period between 20 to 24 may 2022. Based on Worldpop population data and the maximal flood water coverage, ~22,000 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 the United Nations Satellite Centre (UNOSAT).

**UNOSAT code: FL20220526GUY** This map illustrates satellite-detected surface waters along the Rupununi river in Upper Takutu-Upper Essequibo Region of Guyana as observed from a Sentinel-1 image acquired on 26 May 2022 at 09:45 UTC. Within the analyzed area of about 14,000 km2, a total of about 320 km2 of lands were observed as flooded. 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

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: FL20210817GUY** This map illustrates satellite-detected surface waters in the Upper Takutu-Upper Essequibo Regions of Guyana using NOAA-VIIRS between 12th and 16th of August 2021. Within the analysed area of about 24,462 km2, a total of about 1417 km2 of lands were observed as flooded. Based on Worldpop spatial demographic data,approximately 2153 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 UNITAR-UNOSAT.

**UNOSAT code: FL20210520GUY** This map illustrates satellite-detected surface waters along the Rupununi river in Upper Takutu-Upper Essequibo Region of Guyana as observed from Sentinel-1 image acquired on 12 June 2021 at 09:45 UTC. Within the analyzed area of about 6,500 km2, a total of about 235 km2 of lands were observed as flooded and the road about 26 km are potentially affected by the floods. This is a preliminary analysis that 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: FL20210520GUY** This map illustrates cumulative floods using NOAA20-VIIRS data in Guyana between the 2nd and the 6th of June 2021. Within the analyzed cloud free zones of about 200,000 km2, a total of about 5,000 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 80,000 people are potentially exposed or living close to flooded areas. The potentially exposed population is mainly located in the Region 6 with ~22,000 people, Region 3 with ~19,000 and Region 5 with ~15,000 people. This is a preliminary analysis that has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: FL20210520GUY** This map illustrates satellite-detected surface waters along the Mazaruni river in Cuyuni-Mazaruni Region of Guyana as observed from Sentinel-1 radar image acquired on 23 June 2021 at 22:15 UTC. Within the analyzed area of about 480 km2, the water extent appears to have decreased of about 3 km2 since 9 June 2021. This is a preliminary analysis that 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: FL20210520GUY** This map illustrates satellite-detected surface waters in Guyana as observed from a Joint VIIRS-ABI flood product acquired on 6 & 8 June 2021. Within the analyzed cloud free zones of about 140,000 km2, a total of about 2,000 km2 of lands appear to be flooded. The overall water extent as detected on the 8 June 2021 appears to have decreased of about 1,000 km2 since 6 June 2021. Based on Worldpop population data and the detected surface waters, about 30,000 people are potentially exposed or living close to flooded areas. The potentially exposed population is mainly located in the Region 6 with ~10,000 people and Region 2 & 5 with ~6,000 people each. This is a preliminary analysis that has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

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