Iraq GIS data
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aircraft imagery by Ahmed, acquired 2025-08-30 to 2025-08-30, 1.6 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM2 boundaries for Iraq: 101 units, representing 2021. Source: Iraq Central Statistics Office, OCHA Iraq.
geoBoundaries ADM1 boundaries for Iraq: 18 units, representing 2022. Source: geoBoundaries, Wikimedia Commons.
satellite imagery by Ahmed, acquired 2025-12-21 to 2025-12-22, 1.8 cm per pixel, published on OpenAerialMap.
uav imagery by Ahmed, acquired 2025-12-21 to 2025-12-22, 1.8 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM0 boundaries for Iraq: 1 units, representing 2021. Source: Natural Earth.
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 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. 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.
Iraq administrative level 0-3 boundaries (COD-AB) dataset version 01. This dataset is structured into 3 levels: - Admin 1: 18 Governorate - Admin 2: 101 District - Admin 3: 294 Sub-district Dates associated with this dataset: - 19 December 2024: dataset reviewed for accuracy and completeness - 03 June 2019: valid for use by the humanitarian community - 05 June 2019: last edit to the dataset before publishing - 05 June 2019: boundaries created by the source Contributed by OCHA Middle East and North Africa (ROMENA). Quality assured, configured, and published by OCHA Field Information Services (FIS) and HDX. Part of the dataset: Global Subnational Administrative Boundaries
This dataset shows the list of operating health facilities. Attributes included: Name,Nature of Facility, Activities, Lat, Long
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
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
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
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
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
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
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
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
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
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.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
This map illustrates satellite-detected fires and smoke plumes at oil wells south of Mosul, and also east of Baiji, Iraq. The Mosul fires began with an initial fire at one or two wells on 8 May 2016, lasting less than one day, and intermittently burned in June. The current fire complex began on 3 July with daily fire detections occurring until about 12 July, when the fires greatly increased in number and continued to burn until gradual reductions in detected fires occurred starting in November 2016. The fires east of Baiji have been active since early January 2016. The frequency of smoke plumes (in days) is symbolized in shades of red and yellow, and was calculated using daily MODIS satellite images collected between 18 July 2016 and 13 March 2017. Note that as the plume dissipates then areas of thinner smoke are not detected in this process. The inset on the top right corner shows the infrared data from a Landsat image collected on 25 February 2017, indicating the Mosul fires in white. The inset on the top left corner, from 5 March 2017, shows the same area in real color. Additionally, precipitation data from NASA's IMERG algorithm was included to evaluate instances of rainfall intersecting the smoke plume. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.