Cuba GIS data

satellite imagery by Vantor, acquired 2025-10-30 to 2025-10-30, 31.9 cm per pixel, published on OpenAerialMap.

geoBoundaries ADM2 boundaries for Cuba: 168 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

Dates of Images: Post-Event: October 29, 2025; October 30, 2025; October 31, 2025; November 1, 2025; November 2, 2025; November 4, 2025; November 7, 2025; November 8, 2025; November 9, 2025. Pre-Event: September 2025 Please Note: Images with majority cloud cover are not shown in the Map Viewer, but are available in the service. Summary: These Black Marble Day-Night Band (BRDF-Corrected) cloud masks were created by the NASA Black Marble Science team. This is the layer to displaying where clouds are present. This comparison between the images is meant as a visual assessment of outage impacts to aid various partners who are working to deliver emergency aids to local communities. Power outage maps like these help disaster response efforts in the short-term as well as long-term monitoring during the crucial stages of disaster recovery. Suggested Use: NASA's Black Marble Nighttime Light product suite is a state-of-the-art daily global collection of standard products for monitoring nighttime lights (NTL). Utilizing the Visible Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) aboard the Suomi-NPP, NOAA-20, and NOAA-21 satellites. Grey represents cloud cover. Satellite/Sensor: Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (SNPP), NOAA-20, and NOAA-21 Resolution: 500 meters Credits: NASA Black Marble Science team Please cite the following two references when using this data: Román MO, Stokes EC, Shrestha R, Wang Z, Schultz L, Carlo EA, Sun Q, Bell J, Molthan A, Kalb V, Ji C. Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PloS one. 2019 Jun 28;14(6):e0218883. Román MO, Wang Z, Sun Q, Kalb V, Miller SD, Molthan A, Schultz L, Bell J, Stokes EC, Pandey B, Seto KC. NASA's Black Marble nighttime lights product suite. Remote Sensing of Environment. 2018 Jun 1;210:113-43. Point of Contact: Ranjay Shrestha NASA Goddard Space Flight Center E-mail: ranjay.m.shrestha@nasa.gov Additional Links: NASA’s Black Marble Product Suite Román, M.O. et al. (2019) Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS One, 14 (6). Román, M.O. et al. (2018) NASA’s Black Marble nighttime lights product suite. Remote Sensing of Environment. 210, 113–143. REST Endpoint: See URL on the right side of the page. WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202510_HURRICANE_MELISSA/202511_blackmarble_brdf_cloud/ImageServer/WMSServer

satellite imagery by Vantor, acquired 2025-11-02 to 2025-11-02, 31.4 cm per pixel, published on OpenAerialMap.

satellite imagery by Vantor, acquired 2025-11-03 to 2025-11-03, 31.8 cm per pixel, published on OpenAerialMap.

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

geoBoundaries ADM1 boundaries for Cuba: 16 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.

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.

Cuba administrative level 0-2 boundaries (COD-AB) dataset version 01. This dataset is structured into 2 levels: - Admin 1: 16 province - Admin 2: 168 municipio Note: nan Dates associated with this dataset: - 21 June 2019: dataset reviewed for accuracy and completeness - 21 June 2019: valid for use by the humanitarian community - 21 June 2019: last edit to the dataset before publishing - 07 September 2017: boundaries created by the source Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries

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 Cuba **Planet** Road Surface. The corresponding paper is available at: https://arxiv.org/abs/2512.04092 Road Surface Statistics Approximately 0.1302 million km of roads are mapped in OSM in this region. Based on AI-derived estimates: - Paved roads: 0.015 million km (11.5026%) - Unpaved roads: 0.0359 million km (27.5389%) About 0.0794 million km (60.9585%) of road surface information is missing in OSM. To reduce this gap, the Mapillary-derived road surface dataset provides an additional 0.0005 million km (0.6017%) 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

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

# Cuba Hurricane Melissa: Damage and Flood Proxy Maps, Oct 2025 The **Earth Observatory of Singapore – Remote Sensing Lab (EOS-RS)** produced these damage and flood proxy maps for Cuba Hurricane Melissa (Oct 2025) 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: Cuba, Hurricane Melissa, 30 October 2025, v0.9 ** - **EOS-RS Flood Proxy Map: Cuba, Hurricane Melissa, 30 October 2025, v0.9** - **EOS-RS Damage Proxy Map: Cuba, Hurricane Melissa, 29 October 2025, v0.9 ** - **EOS-RS Flood Proxy Map: Cuba, Hurricane Melissa, 29 October 2025, v0.9** **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 (2025)

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.

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

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

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

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

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

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

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

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

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.

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 TC20221001CUB, GDACS Id: 1000923** This map illustrates satellite-detected surface waters in Pinar del Río province, Cuba as observed from a Sentinel-1 imagery acquired on 28 September 2022 at 07:27 local time. Within the analyzed area of about 1,300 km², a total of about 27 km² of lands appear to be affected with flood waters amongst which 13 km² are flooded croplands. Based on Worldpop population data and the detected surface waters, about 4,200 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 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.

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.

This map illustrates potential satellite-detected damaged structures in Maisi town and the surrounding area in Guantanamo Province, Cuba. The UNITAR-UNOSAT analysis used a Pleiades satellite image, acquired on the 7 October 2016, and identified 417 damaged structures within the map extent; 230 were identified inside the town of Maisi .This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potential satellite-detected damaged structures in Baracoa town, in Guantanamo Province, Cuba. The UNITAR-UNOSAT analysis used a Pleiades satellite image, acquired on the 11 October 2016, and identified 3,147 damaged structures inside the town of Baracoa.This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

Population Exposure Wind Speed Zones, Cuba

This map illustrates potential satellite-detected damaged structures in Imias and the surrounding area in Guantanamo Province, Cuba. The UNITAR-UNOSAT analysis used a Pleiades satellite image, acquired on the 10 October 2016, and identified 552 damaged structures within the map extent and 603 damaged structures in the covered area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

This map illustrates potential satellite-detected structures in Cajobabo town and the surrounding area in Guantanamo Province, Cuba. The UNITAR-UNOSAT analysis used a Pleiades satellite image, acquired on the 7 of October, 2016. The analysis identified 450 damaged structures within the map extent of which 211 were identified inside the town of Cajobabo. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

This report describes preliminary building damage analysis carried out by UNITAR-UNOSAT covering the towns and its surrounding areas over Baracoa, Maisi, Imias, and Cajobabo. Building damage analysis was conducted with the post-disaster satellite images (Pleiades acquired on 7/10/2016, 10/10/2016, and 11/10/2016).

**ADAM ID: **sm\_us6000sgrt Magnitude 5.8 earthquake at 11.634 depth occurred on Mar 17 2026 40km SE of Imías. The epicentre of the earthquake was located at latitude 19.8399 longitude -74.3586. It impacted 51102 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.