United Nations map (known as UNmap) is a worldwide geospatial database consisting of country and geographic name information on a global scale. The data is designed for the production of cartographic documents and maps, including their dissemination via public electronic networks, for the Secretariat of the United Nations.The United Nations maintains the Data as a courtesy to those who may choose to access the Data. The Data is provided “as is”, without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose and non-infringement.

Disclaimers: - The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations. - The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. - Dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. - The final status of Jammu and Kashmir has not yet been agreed upon by the parties. - Final boundary between the Republic of Sudan and the Republic of South Sudan has not yet been determined. - Final status of the Abyei area is not yet determined. - A dispute exists between the Governments of Argentina and the United Kingdom of Great Britain and Northern Ireland concerning sovereignty over the Falkland Islands (Malvinas).

Generalization parametrisation for the data is developed based on the work of Douglas and Peucker (1973), Wang (1996) and the Polynomial Approximation with Exponential Kernel algorithm.The adequate generalized data should be used for the intended dissemination scale and not rely on software or platform-automated generalization as some specific geographic features are removed at scales. For instance, the region of Abyei is not included at the scale of 1:25 million but is included at lower scales.

Maps produced using this layer should be featured with the appropriate disclaimer depending on the shown area.

Source: United Nations International and Administrative Boundaries Resources

International boundaries provided by United Nations Clear Map. The United Nations Clear Map (hereinafter “Clear Map”) is a background reference web mapping service produced to facilitate “the issuance of any map at any duty station, including dissemination via public electronic networks such as Internet” and “to ensure that maps meet publication standards and that they are not in contravention of existing United Nations policies” in accordance with the in the Administrative Instruction on “Regulations for the Control and Limitation of Documentation – Guidelines for the Publication of Maps” of 20 January 1997 (http://undocs.org/ST/AI/189/Add.25/Rev.1) Clear Map is created for the use of the United Nations Secretariat and community. All departments, offices and regional commissions of the United Nations Secretariat including offices away from Headquarters using Clear Map remain bound to the instructions as contained in the Administrative Instruction and should therefore seek clearance from the UN Geospatial Information Section (formerly Cartographic Section) prior to the issuance of their thematic maps using Clear Map as background reference. Disclaimers: The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Credits (Attribution) Produced by: United Nations Geospatial Contributor: UNGIS, UNGSC, Field Missions CONTACT US: Your feedback is appreciated and should be sent directly to: Email:Clearmap@un.org / gis@un.org (UNCLASSIFIED) © UNITED NATIONS 2018 More information on the United Nations Clear Map website at https://geoportal.dfs.un.org/arcgis/sharing/rest/content/items/541557fd0d4d42efb24449be614e6887/data

This is a PDF format map of the country, as released by the United Nations.

This is a PDF format map of the country, as released by the United Nations.

International place labels based on the M49 “Standard Country or Area Codes for Statistical Use” by the Statistics Division of the United Nations Secretariat. The "Place Labels" layer is derived from the centroids of the 'UN Country Boundaries of the World" polygon dataset to which country names from the United Nations Terminology Database (UNTERM) have been associated. The centroid points are used to move the labels in appropriate locations within the country boundaries. Credits: UN Statistics Division - https://unstats.un.org/unsd/methodology/m49/#fn2 Disclaimer: The designations employed and the presentation of material in this dataset does not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

The United Nations Geospatial Data, or Geodata, is a worldwide geospatial dataset of the United Nations. The United Nations Geodata is provided to facilitate the preparation of cartographic materials in the United Nations includes geometry, attributes and labels to facilitate the adequate depiction and naming of geographic features for the preparation of maps in accordance with United Nations policies and practices. The geospatial dataset include polygons/areas of countries (BNDA_simplified). Please refer this page for more information.

UNOSAT code: FL20241022SEN This application provides geospatial information regarding the floods in Senegal

Important note: The boundaries and names shown, and the designations used on this map do not imply official endorsement or acceptance by the United Nations. The United Nations Satellite Centre. UNOSAT is not responsible for the misuse or misrepresentation of the map.

The product data are six statistics that were estimated for the chemical concentration of lithium in the soil C horizon of the conterminous United States. The estimates are made at 9998 locations that are uniformly distributed across the conterminous United States. The six statistics are the mean for the isometric log-ratio transform of the concentrations, the equivalent mean for the concentrations, the standard deviation for the isometric log-ratio transform of the concentrations, the probability of exceeding a concentration of 55 milligrams per kilogram, the 0.95 quantile for the isometric log-ratio transform of the concentrations, and the equivalent 0.95 quantile for the concentrations. Each statistic may be used to generate a statistical map that shows an attribute of the distribution of lithium concentration.

This is a PDF format map of the country, as released by the United Nations.

The tls_all_background dataset provides comprehensive geospatial information for Timor-Leste. This dataset is sourced from bkk_tls_data. It is designed to support a wide range of applications, including urban planning, environmental monitoring, and infrastructure development. The dataset offers detailed background mapping to ensure accuracy and reliability for projects requiring geospatial data in this area. For additional context and technical details, please refer to the linked repository.

The Administrative Boundaries used by the Data in Emergencies Hub are the result of a collection of international and subnational divisions currently used by FAO country offices for mapping and reporting purposes. With only a few exceptions, they are mostly derived from datasets published on The Humanitarian Data Exchange (OCHA).The dataset consists of national boundaries, first subdivision, and second subdivision for Sure! Here's the reformatted list as requested:

Afghanistan, Angola, Bangladesh, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Democratic Republic of the Congo, Ecuador, El Salvador, Federated States of Micronesia, Ghana, Guatemala, Haiti, Honduras, Iraq, Kingdom of Tonga, Kiribati, Kyrgyzstan, Lao People's Democratic Republic, Lebanon, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Namibia, Nepal, Niger, Nigeria, Pakistan, Palestine, Philippines, Republic of the Marshall Islands, Saint Lucia, Samoa, Senegal, Sierra Leone, Solomon Islands, Somalia, South Sudan, Sri Lanka, Sudan, Suriname, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Togo, Tuvalu, Uganda, Ukraine, Venezuela, Vietnam, Yemen, and Zimbabwe.In the Feature Layer, the administrative boundaries are represented by closed polygons, administrative levels are nested and multiple distinct polygons are represented as a single record.The Data in Emergencies Hub team is responsible for keeping the layer up to date, so please report any possible errors or outdated information.The boundaries and names shown and the designations used on these map(s) do not imply the expression of any opinion whatsoever on the part of FAO concerning the legal status of any country, territory, city, or area or of its authorities, or concerning the delimitation of its frontiers and boundaries. Dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The final boundary between the Sudan and South Sudan has not yet been determined. The final status of the Abyei area is not yet determined. The dotted line represents approximately the Line of Control in Jammu and Kashmir agreed upon by India and Pakistan. The final status of Jammu and Kashmir has not yet been agreed upon by the parties.

This is an excel mapping tool that was built based on Cuba administrative boundaries (admin2) - extracted from the GADM database (www.gadm.org), version 2.8, November 2015. Available on HDX: https://data.humdata.org/dataset/cuba-administrative-boundaries-levels-0-and-1-from-gadm). The population dataset is a sample data. The tool is built to help people to quickly map their datasets.

Switzerland's accession to the United Nations. Map types: Lines, Choropleths. Spatial extent: Switzerland. Time: 2002. Spatial units: Cantons, Communes

This is a PDF format map of the country, as released by the United Nations.

As part of THEIA (the French Data and Services center for continental surfaces) CIRAD's TETIS research unit is developing an automated mapping method based on the Moringa chain that minimizes interactions with users by automating most image analysis and processing. The methodology uses jointly a Very High Spatial Resolution image (Spot6/7 or Pleiades) and one or more time series of High Spatial Resolution optical images such as Sentinel-2 and Landsat-8 for a classification combining segmentation and object classification (use of the Random Forest algorithm) driven by a learning database constituted from in situ collection and photo-interpretation. The land use maps are produced as part of the GABIR project (Gestion Agricole des Biomasses à l'échelle de l'Ile de la Réunion) and are all distributed on CIRAD's spatial data catalogue in Réunion: http://aware.cirad.fr/ This Dataverse entry concerns the maps produced, for the year 2017, using a mosaic of Pleiades images to calculate segmentation (extraction of homogeneous objects from the image). We use a field database with a nested nomenclature with 3 levels of accuracy allowing us to produce a classification by level. The most detailed level distinguishing crop types has an overall accuracy of 86% and a Kappa index of 0.85. Level 2, distinguishing crop groups, has an overall accuracy of 92% and a Kappa index of 0.90. Level 1, distinguishing major land use groups, has an overall accuracy of 97% and a Kappa index of 0.94. A detailed sheet presenting the validation method and results is available for download. Dans le cadre du Centre d’Expertise Scientifique Occupation des Sols de THEIA, l’UMR TETIS du CIRAD développe une méthode de cartographie automatisée fondée sur la chaine Moringa qui minimise les interactions avec les utilisateurs par l’automatisation de la plupart des processus d’analyse et de traitement des images. La méthodologie utilise conjointement une image à Très Haute Résolution Spatiale (Spot6/7 ou Pléiades) et une ou plusieurs séries temporelles d’images optiques à Haute Résolution Spatiale type Sentinel-2 et Landsat-8 pour une classification combinant segmentation et classification objet (utilisation de l’algorithme Random Forest) entrainée par une base de données d’apprentissage constituée à partir de collecte in situ et de photo-interprétation. Les cartes d'occupation du sol sont réalisées dans le cadre du projet GABIR (Gestion Agricole des Biomasses à l’échelle de l'Ile de la Réunion) et sont toutes diffusées sur le catalogue de données spatiales du Cirad à la Réunion : http://aware.cirad.fr/ Cette fiche du Dataverse concerne les cartes produites, pour l'année 2017, en utilisant une mosaïque d'images Pléiades pour calculer la segmentation (extraction d'objets homogènes à partir de l'image). Nous utilisons une base de données terrain ayant une nomenclature emboitée avec 3 niveaux de précision nous permettant de produire une classification par niveau. Le niveau le plus détaillé distinguant les types de cultures présente une précision globale de 86% et un indice de Kappa est de 0,85. Le niveau 2, distinguant les groupes de cultures présente une précision globale de 92% et un indice de Kappa est de 0,90. Le niveau 1, distinguant les grands groupes d'occupation du sol présente une précision globale de 97% et un indice de Kappa est de 0,94. Une fiche détaillée présentant la méthode et les résultats de validation est téléchargeable.

The Global Administrative Unit Layers (GAUL) is an initiative implemented by FAO within the Bill & Melinda Gates Foundation, Agricultural Market Information System (AMIS) and AfricaFertilizer.org projects. The GAUL compiles and disseminates the best available information on administrative units for all the countries in the world, providing a contribution to the standardization of the spatial dataset representing administrative units. The GAUL always maintains global layers with a unified coding system at country, first (e.g. departments) and second administrative levels (e.g. districts). Where data is available, it provides layers on a country by country basis down to third, fourth and lowers levels. The overall methodology consists in a) collecting the best available data from most reliable sources, b) establishing validation periods of the geographic features (when possible), c) adding selected data to the global layer based on the last country boundaries map provided by the UN Cartographic Unit (UNCS), d) generating codes using GAUL Coding System and e) distribute data to the users (see TechnicalaspectsGAUL2015.pdf). Because GAUL works at global level, unsettled territories are reported. The approach of GAUL is to deal with these areas in such a way to preserve national integrity for all disputing countries (see TechnicalaspectsGAUL2015.pdf and G2015_DisputedAreas.dbf). GAUL is released once a year and the target beneficiary of GAUL data is the UN community and other authorized international and national partners. Data might not be officially validated by authoritative national sources and cannot be distributed to the general public. A disclaimer should always accompany any use of GAUL data. 5 territories have been updated respect to the previous release. Moreover, the coastline of American countries or other special areas have been updated using Open Street Map (see ReleaseNoteGAUL2015.pdf). GAUL keeps track of administrative units that has been changed, added or dismissed in the past for political causes. Changes implemented in different years are recorded in GAUL on different layers. For this reason the GAUL product is not a single layer but a group of layers, named "GAUL Set" (see ReleaseNoteGAUL2015.pdf). GAUL 2015 is the eighth release of the GAUL Set, which now includes the following files: - G2015_2014: these file refer the global layer to year 2014. - G2015_2013: these file refer the global layer to year 2013. - G2015_2012: these file refer the global layer to year 2012. - G2015_2011: these file refer the global layer to year 2011. - G2015_2010: these file refer the global layer to year 2010. - G2015_2009: these file refer the global layer to year 2009 - G2015_2008: these file refer the global layer to year 2008. - G2015_2007: these file refer the global layer to year 2007. - G2015_2006: these file refer the global layer to year 2006. - G2015_2005: these file refer the global layer to year 2005. - G2015_2004: these file refer the global layer to year 2004. - G2015_2003: these file refer the global layer to year 2003. - G2015_2002: these file refer the global layer to year 2002. - G2015_2001: these file refer the global layer to year 2001. - G2015_2000: these file refer the global layer to year 2000. - G2015_1999: these file refer the global layer to year 1999. - G2015_1998: these file refer the global layer to year 1998. - G2015_1997: these file refer the global layer to year 1997. - G2015_1996: these file refer the global layer to year 1996. - G2015_1995: these file refer the global layer to year 1995. - G2015_1994: these file refer the global layer to year 1994. - G2015_1993: these file refer the global layer to year 1993. - G2015_1992: these file refer the global layer to year 1992. - G2015_1991: these file refer the global layer to year 1991. - G2015_1990: these file refer the global layer to year 1990. The GAUL project does not implement changes dated before 1990.

The land cover maps published here were produced for four regions of the northern part of Chin state in Myanmar: Hakha, Falam, Tedim and Thantlang. This work was carried out as part of the ALIVE FNS project to monitor land use (forests, cultivated land, built-up areas, etc.). We used the Moringa processing chain, which is based on satellite imagery (Sentinel 2 free of charge time series and SPOT6-7 very high spatial resolution) and a supervised classification algorithm (Random Forest) trained on a reference database made of polygons associated with a land cover class. Generally, this database comes from ground GPS surveys, but it can be replaced by photo-interpretation of very high spatial resolution images if field collection is unavailable or impossible, as it is the case here in the State of Chin. The database was therefore obtained by photo-interpretation of Spot6/7 images acquired as part of the Dinamis programme. The nomenclature includes 4 crop classes (irrigated crops - mainly rice, shifting cultivation, new shifting cultivation, old shifting cultivation) and 6 non-crop classes (open spaces with little or no vegetation, herbaceous vegetation, shrubland, wooded vegetation, water, built-up areas). The maps are available, for the years 2020 and 2021, at a spatial resolution of 1.5 m over the parts covered by SPOT6/7 imagery (approximately half of the study area) and at a spatial resolution of 10m using only Sentinel-2 imagery over the whole area comprising the 4 regions: Hakha, Falam, Tedim, Thantlang. The overall and class accuracies (f-score) of the maps are available in a text file included in the archive containing the maps. Les cartes d'occupation du sol diffusées ici ont été produites sur quatre régions situées au Nord de l’état du Chin au Mynanmar : Hakha, Falam, Tedim, Thantlang. Ces travaux ont été réalisés dans le cadre du projet ALIVE FNS pour observer l'occupation des sols (forêts, terres cultivées, surfaces bâties, etc.). Nous avons utilisé la chaine Moringa qui s'appuie sur l'imagerie satellite (Sentinel 2 et SPOT6-7) et un algorithme de classification supervisée entraîné à partir d'une base de données de référence représentative de l'occupation des sols. Généralement, cette base de données est constituée à partir de relevés GPS sur le terrain, mais elle peut être remplacée par une photo-interprétation sur des images à très haute résolution spatiale si la collecte sur le terrain n'est pas disponible ou impossible, comme c'est le cas ici dans l'État de Chin. La base de données a donc été obtenue par photo-interprétation d’images Spot56/7 acquises dans le cadre du dispositif Dinamis. La nomenclature comprend 4 classes de cultures (cultures irriguées - principalement le riz, cultures itinérantes, nouvelles cultures itinérantes, anciennes cultures itinérantes) et 6 classes de non-cultures (espaces ouverts avec peu ou pas de végétation, végétation herbacée, zones arbustives, végétation boisée, eau, surfaces bâties). Les cartes sont disponibles, pour les années 2020 et 2021, à une résolution spatiale de 1,5m sur les parties couvertes par l'imagerie SPOT6/7 (non gratuites) et à une résolution spatiale de 10m utilisant uniquement des images Sentinel-2 (gratuites) sur une zone plus grande comprenant l’ensembles dans 4 régions : Hakha, Falam, Tedim, Thantlang. Les précisions globales et par classes des cartes sont disponible dans un fichier texte inclus dans l’archive contenant les cartes.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Iraq data available from WorldPop here.

This is a PDF format map of the country, as released by the United Nations.