Ukraine GIS data

geoBoundaries ADM2 boundaries for Ukraine: 495 units, representing 2006. Source: geoBoundaries, Wikimedia Commons.

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

geoBoundaries ADM1 boundaries for Ukraine: 27 units, representing 2017. Source: OpenStreetMap, Wambacher. Open Data Commons Open Database License 1.0

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.

These data were produced by the WorldPop Research Group at the University of Southampton. These data include gridded estimates of population at approximately 100m and 1km resolution for 2020, along with estimates of the number of people belonging to individual age-gender groups. These results were produced using Subnational Population Statistics 2020 for Ukraine provided in the Common Operational Dataset on Population Statistics (COD-PS) and ORNL LandScan HD for Ukraine 2022 settlement layer. The datasets are produced using the "top-down" method, with both the unconstrained and constrained top-down disaggregation methods used to produce two different datasets. The differences between constrained and un-constrained methods are described here . **Main data sources** - Subnational Population Statistics for Ukraine provided by Common Operational Dataset on Population Statistics (COD-PS). The subnational population statistics were estimated using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since the last census. - Settlement layer ORNL LandScan HD for Ukraine. - Subnational Administrative Boundaries for Ukraine provided by OCHA. - Geospatial covariate layers available at WorldPop. For further details, please, read the Release Statement. **Release content** 1. ukr_pop_2020_100m_unconstrained_v1_0.zip 2. ukr_pop_2020_100m_constrained_v1_0.zip 3. ukr_pop_2020_1km_unconstrained_v1_0.zip 4. ukr_pop_2020_1km_constrained_v1_0.zip 5. ukr_agegender_2020_100m_unconstrained_v1_0.zip 6. ukr_agegender_2020_100m_constrained_v1_0.zip 7. ukr_agegender_2020_1km_unconstrained_v1_0.zip 8. ukr_agegender_2020_1km_constrained_v1_0.zip 9. ukr_agegender_0_18_2020_1km_unconstrained_v1_0.zip 10. ukr_agegender_0_18_2020_1km_constrained_v1_0.zip **Recommended citations** Bondarenko M., Sorichetta A., Leasure DR. and Tatem AJ. 2022 *Gridded population estimates for Ukraine using UN COD-PS estimates 2020, version 1.0*. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00734 **License** *These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information*. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

**For real-time data downloads, go to LogIE Ukraine operation page** This dataset contains layers ESRI ArcGIS Online feature services for the following layers of interest to the logistics cluster in Ukraine: - Entry and exit points - Ports - Supply routes - Roads - Railways - Reception points - Roads in Poland

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.

These data were produced by WorldPop at the University of Southampton and the ‘Smart Cities and Spatial Development’ team at the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR). These data include gridded estimates of population at approximately 100m and 1km resolution for 2020, along with estimates of the number of people belonging to individual age-gender groups. These results were produced using subnational population estimates for Ukraine in 2020 provided in the Common Operational Dataset on Population Statistics (COD-PS) and building height/area/fraction/volume covariates extracted from the World Settlement Footprint (WSF) imperviousness and WSF-3D by DLR. The constrained top-down disaggregation method was used to produce the datasets. The modelling work and geospatial data processing was led by Bondarenko M., Palacios-Lopez D., Sorichetta A., Leasure D.R., ,Zeidler J., Marconcini M., and Esch T.. Oversight was provided by Tatem A.J. Internal WorldPop peer reviews that helped to improve the results and documentation was provided by Lazar A.N.. **Main data sources** - The German Aerospace Centre’s (DLR) WSF imperviousness and WSF 3D products (WSF-3D). - Subnational population estimates for Ukraine in 2020 provided in the Common Operational Dataset on Population Statistics (COD-PS). The subnational population estimates were produced using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since then. - Subnational Administrative Boundaries for Ukraine provided by OCHA . - Geospatial covariate layers available at WorldPop. For further details, please, read the Release Statement. **Release content** 1. ukr_pop_2020_100m_constrained_v2.zip 2. ukr_pop_2020_1km_constrained_v2.zip 3. ukr_agegender_2020_100m_constrained_v2.zip 4. ukr_agegender_2020_1km_constrained_v2.zip 5. ukr_agegender_0_18_2020_100m_constrained_v2.zip 6. ukr_agegender_0_18_2020_1km_constrained_v2.zip **Recommended citations** Bondarenko M., Palacios-Lopez D., Sorichetta A., Leasure D.R., Zeidler J., Marconcini, M., Esch T., and Tatem A.J. 2022 *Gridded population estimates for Ukraine using UN COD-PS estimates 2020, version 2.0*. WorldPop and DLR, University of Southampton. doi:10.5258/SOTON/WP00735 **License** *These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information*. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

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.

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

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

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

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

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

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

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

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

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

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

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.

Natural Earth admin 0 countries ukr at 1:10 million scale: public domain cartographic vector data built to make small-scale world, regional and country maps.

The dataset contains entry/exit crossing points along the "contact line" dividing Government-controlled area (GCA) and non-Government-controlled area (NGCA) with coordinates and operational status (operational, non-operational, partially operational, planned). Source: State Border Guard Service of Ukraine, https://dpsu.gov.ua/ua/perelik-kpvv/.

The shapefile contains areas of Donetska and Luhanska oblasts divided by the "contact line" (as of August 2018) in the Government-controlled area (GCA) and non-Government-controlled area (NGCA).

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 actual boundaries were most recently adjusted in: January 2025. Boundary information source: State Scientific Production Enterprise “Kartographia”. Contributor: OCHA Field Information Services Section (FISS). COD-AB quality level: cod-enhanced (geometry and attributes verified and standardized) and live services available. https://codgis.itos.uga.edu/arcgis/rest/services/COD_External/UKR_pcode/FeatureServerfeature server. Most recent COD-AB review conclusion: The COD-AB does not require any update. OCHA country status: Operational (country office). An edge-matched (COD-EM) dataset is available Most recent COD-AB review date: January 2025. Deepest administrative level: ADM4. COD-PS HDX URL: https://data.humdata.org/dataset/cod-ps-ukr. Deepest administrative level with complete coverage: ADM3. COD-EM HDX link: https://data.humdata.org/dataset/cod-em-ukr. Administrative level required by humanitarian community: ADM2. Note: The Ukrainian government has not fully implemented its new administrative structure reforms in the Autonomous Republic of Crimea [UA01] and Sevastopol [UA85]. Consequently, 31 ADM4 features within the Sevastopol ADM3 polygon retain P-codes that do not correctly conform to [UA85]. This will be rectified in a COD-AB update once the government has regained control of the Crimean Peninsula. Note: Some administrative feature names were adjusted in a September/October 2024 administratrive feature renaming exercise. See reference table at https://data.humdata.org/dataset/cod-ab-ukr. Note: The Ukraine COD-AB was most recently adjusted in early 2024 - affecting administrative level 3 (hromadas) only. Note: The population statistics common operational dataset (COD-PS-UKR) is only available via special request. See Ukraine - Subnational Population Statistics. Users should use the 'Request Data' button. Note: Limitations of the Ukrainian-to-Russian transliteration method mean that more than one Ukrainian name may be represented by a single Russian feature name. Note: Feature name attribute fields in this dataset incorrectly use 'UA' to represent the Ukrainian language. The correct language code is 'UK'. This will be resolved in 2026.

Ukraine administrative level 0-4 boundaries (COD-AB) dataset. NOTE: The population statistics common operatioanl dataset (COD-PS-UKR) is only available vis special request. See Ukraine - Subnational Population Statistics. Users should use the 'Request Data' button. This COD-AB was most recently reviewed for accuracy and necessary changes in April 2024. The COD-AB does not require any update. Sourced from State Scientific Production Enterprise “Kartographia” Vetting by Information Technology Outreach Services (ITOS) with funding from USAID. This COD-AB is suitable for database or GIS linkage to the Ukraine COD-PS. An edge-matched (COD-EM) version of this COD-AB is available on HDX here: Please see the COD Portal. Administrative level 1 contains 27 feature(s). Administrative level 2 contains 139 feature(s). Administrative level 3 contains 1769 feature(s). Administrative level 4 contains 29707 feature(s).

**UNOSAT code FL20230606UKR** This map illustrates satellite-detected flood waters and affected structures over Hola Prystan City, in the east bank of the Dnieper/Dnipro River in Khersonska Oblast, Ukraine as observed from a WorldView-3 satellite images acquired on 17 June 2023 at 09:13 UTC and 8 June 2023 at 08:35 UTC. Within the analysed area of 11 km², about 27% (3 km²) of Hola Prystan City area appears to be flooded. Water extent appears to have decreased by about 5 km² since 8 June 2023. About 1,300 structures were still affected by floods on 17 June 2023. 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 FL20230606UKR** This map illustrates satellite-detected flood waters and affected structures over Kardashynka and Kokhany Villages and surroundings in Khersonska Oblast (Ukraine) as detected from a WorldView-3 image acquired on 7 June 2023 at 08:19 UTC; and ICEYE image acquired on 7 June 2023. Within the analysed area of 27 km², about 81% (22 km²) of Kardashynka Village, and Kokhany Village and surrounding area appears to be flooded. 8,728 structures and 38 harbours are affected. 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 FL20230606UKR** This map illustrates satellite-detected flood waters and affected structures over Antonivka City and Dachi City, Khersonskyi Region, Khersonska Oblast (Ukraine) as observed from a WorldView-3 image acquired on 7 June 2023 at 08:19 UTC; and ICEYE image acquired on 7 June 2023. Within the analysed area of 15 km², about 100% (2 km²) of Dachi City area appears to be flooded. 1,366 structures and 93 harbours are affected. About 23% (3 km²) of Antonivka City area appears to be flooded. 659 structures and 2 harbours are affected. 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 FL20230606UKR** This map illustrates satellite-detected flood waters and affected structures over Kherson City, Khersonskyi Region, Kehersonska Oblast (Ukraine) as observed from WorldView-3 image acquired on 7 June 2023 at 08:19 UTC; and ICEYE image acquired on 7 June 2023. Within the analysed area of 64 km², about 19% (12 km²) of Kherson City area appear to be flooded. 4,302 structures and 76 harbours are affected. 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 FL20230606UKR** This map illustrates satellite-detected flood waters and affected structures over Oleshky City, Khersonskyi Region, Khersonska Oblast (Ukraine) as observed from WorldView-3 image acquired on 7 June 2023 at 08:19 UTC; and ICEYE, Sentinel-2, Sentinel-2, Sentinel-3 and Landsat-9 images acquired on 6 to 9 June 2023. Within the analysed area of 16 km², about 63% (10 km²) of Obleshky City area appear to be flooded. 8,016 structures and 3 harbours are affected. 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 FL20230606UKR** This map illustrates satellite-detected flood waters and affected structures over Kherson City, Khersonskyi Region, Khersonska Oblast (Ukraine) as observed from WorldView-3 image acquired on 17 June 2023 at 09:13 UTC; and ICEYE image acquired on 7 June 2023. Within the analysed area of 64 km², about 2% (1.5 km²) of Kherson City area appear to be flooded. Water extent appears to have decreased by about 17 km² since 7 June 2023. About 861 structures were still affected by floods on 17 June 2023. 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 FL20230606UKR** This map illustrates satellite-detected surface waters over Khersonska Oblast, Ukraine derived from the MODIS Near Real-Time Global Flood Product using data acquired on 7 June 2023 12:43 UTC; and a Sentinel-2 image acquired on 3 & 5 June 2023 to assess pre-flood conditions. Within the area downstream of the damaged dam about 288 km² of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).