AndreaBozzo/ceres-open-data-index huggingface dataset:
Village Points  Mandalay Region PCode v9.6

Village locations (points) in Mandalay Region, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points Nay Pyi Taw Union Territory PCode v9.6

Village locations (points) in Nay Pyi Taw Union Territory, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points  Mon State PCode v9.6

Village locations (points) in Mon State, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points Shan State PCode v9.6

Village locations (points) in Shan State, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points  Magway Region PCode v9.6

Village locations (points) in Magway Region, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points  Rakhine State PCode v9.6

Village locations (points) in Rakhine State, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points  Kachin State PCode v9.6

Village locations (points) in Kachin State, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points  Chin State PCode v9.6

Village locations (points) in Chin State, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points Bago Region PCode v9.6

Village locations (points) in Bago Region, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points Tanintharyi Region PCode v9.6

Village locations (points) in Tanintharyi Region, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Village Points Kayah State PCode v9.6

Village locations (points) in Kayah State, based on the latest MIMU Pcode version 9.6 . Place names from General Administration Department (GAD), are available in Myanmar Unicode. Transliteration in roman script by MIMU.

Place names are in line with the general cartographic practice to reflect the names of such places as designated by the host governments. The United Nations encourages authorities to review place names which include terms that may be viewed or perceived as derogatory.

Tags: geodata, populated places-settlements
Organization: Myanmar Information Management Unit (MIMU)
Sahel Crops Vegetation and Standardized Precipitation Index

Dataset containing information related to precipitation indices useful for analyzing and comparing variations in precipitation over time and across different geographical regions, alongside indices used in the analysis of vegetation and crops to monitor their activity and detect their presence in the territory. Key variables include the Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Structure Insensitive Pigment Index (SIPI) for monitoring vegetation. For rainfall information, the Standardized Precipitation Index (SPI1, SPI3, SPI6, SPI9, and SPI12) is used, where SPI1 to SPI3 are indicators for immediate impacts and SPI6 to SPI12 measure medium-term impacts. Each index is based on data that can be collected through remote sensing, such as satellite imagery, and is designed to measure different characteristics of vegetation and crops. The data are categorized by country, department, and municipality and by year and month, referring to the monthly average. 

This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence.

For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel.


Tags: agriculture-livestock, climate hazards, climate-weather
Organization: Acción contra el hambre - GIS4tech
Sahel Agroclimatics Threats Data

A summary table is established at the municipal level with the list of agroclimatic hazards calculated from the agroclimatic indicators shown in the Vulnerability panel. For each column, the threat category in which the municipality would be found is defined (No risk, Mild threat, Moderate threat, Severe threat), and the final column represents the general summary of global threats, obtained using the 20% rule. The calculation made within each category is established through a matrix of crossed conditions.

This information belongs to the Food and Nutrition Security Monitoring and Prediction System in the Sahel (PREDISAN) Project, funded by the Agencia Andaluza de Cooperación Internacional para el Desarrollo (AACID) and the University of Granada (UGR). The PREDISAN AI-SAHEL Project focuses on the Monitoring and Prediction System for Humanitarian Vulnerability of Pastoral and Agro-pastoral Populations in the Western Sahel, based on GIS Analysis and Artificial Intelligence.

For more information, contact GIS4Tech at info@gis4tech.com or visit the PREDISAN platform at https://predisan.gis4tech.com/sahel.

Tags: climate hazards, climate-weather, socioeconomics
Organization: Acción contra el hambre - GIS4tech
Mozambique: Operational Presence

The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working and what they are doing in order to identify gaps and plan for future humanitarian response. The data collected from the clusters and organizations involved in the response is consolidated by OCHA Mozambique.

Tags: hxl, indicators, operational presence, who is doing what and where-3w-4w-5w
Organization: OCHA Mozambique
Satellite detected water extents between 4 and 8 February 2022 over South Sudan

**UNOSAT code: FL20220119SSD**  This map illustrates cumulative satellite-detected water using VIIRS in South Sudan between 4 to 8 Feb. 2022 compared with the period between 15 to 19 Jan. 2022. 
Within the cloud free analyzed areas of about 630,000 km2, a total of about 28,800 km2 of lands appear to be affected with flood waters. 
Water extent appears to have decreased of about 2,900 km2 since the period between 15 to 19 Jan. 2022. 
Based on Worldpop population data and the maximal flood water coverage, ~684,000 people are potentially exposed or living close to flooded areas.

This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).


Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Landslide analysis eThekwini, Metropolitan Municipality, KwaZulu-Natal Province, South Africa as of 28 April 2022

**UNOSAT code: FL20220418ZAF**  This map illustrates satellite-detected landslides/mudflow in eThekwini, Metropolitan Municipality, eThekwini District, KwaZulu-Natal Province, South Africa as observed from a Sentinel-2 image acquired on 28 April 2022.Within the analyzed area, 11 ha of landslide scars were observed. Based on Worldpop population data, about 27,000 people live inside the analyzed area.
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected waters in Peten Department of Guatemala & Chiapas State of Mexico as of 22 November 2020

**UNOSAT code: TC20201119GTM**  This map illustrates satellite-detected surface waters in Peten, Alta Verapaz and Quiche departments of Guatemala & Chiapas state of Mexico as observed from a Sentinel-1 image acquired on 22 November 2020 at 05:53 Local time. Within the analyzed area of about 10,000 km2, a total of about 380 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 24,000 people are potentially exposed or living close to flooded areas.   
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. 
Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas  due to backscattering properties of the radar signal. 

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Mariupol Updated Building Damage Assessment Overview Map - Livoberezhnyi and Zhovtnevyi Districts

**UNOSAT code: CE20220223UKR**  This map illustrates a satellite imagery based damage analysis within an area of interest (AOI) in the residential area of Mariupol City, Ukraine.

Based on imagery collected on 7, 8, 12 May 2022 and 14 March 2022,  and 21 June 2021, analysts found that 5,647 structures sustained visible damage in the AOI. This represents approximately 32% of the structures. Out of these, 315 are destroyed, 2,132 severely damaged, 3,002 moderately damaged and 194 possibly damaged. Compared to the 14 March 2022 analysis, 5% of previously affected buildings sustained additional damage, and the total damage has increased by 28%, with 4,910 new damaged buildings. While no complete count of buildings for Mariupol is available, an open source dataset, which is visibly incomplete indicates at least 17,568 structures in the area.

This analysis is based on structures visibly damaged as of 12, 8, 7 May 2022 and 14 March 2022 as seen in marginally degraded satellite imagery. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Tags: complex emergency-conflict-security, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Kenya - Socio-economic assessment of refugees in Kenya's Kakuma camp 2015

Kakuma Refugee Camp is one of the longest-standing humanitarian settlements in sub-Saharan Africa and one of the largest refugee camps in the world. In response to recent reductions in funding for the Kenyan refugee operation, increased global competition for funds, and a common belief that not all refugees in such protracted situations have the same humanitarian assistance needs, UNHCR and WFP decided to undertake a study among refugees in Kakuma Camp. The main aims were to fill knowledge gaps regarding refugee livelihoods and the level and differences of vulnerability in refugee households, as well as to explore the feasibility of delivering more differentiated assistance and to identify the mechanisms that would need to be put in place to do so. Specifically, the study was meant to determine whether a permanent targeting mechanism could be put in place or whether other ways of prioritising assistance exist when resources are tight. The survey covered 2,000 households and was implemented in November/December 2015.

Tags: asylum seekers, conflict-violence, displacement, refugees
Organization: UNHCR - The UN Refugee Agency
Democratic Republic of the Congo:  Displacement - [returnees] - Site Assessment [OCHA]

L’extrait de données mouvement de population (Retour) produit chaque mois est fait suivant la [méthodologie](https://drive.google.com/file/d/1IgrUr-8s2-5SqVKl9Q1ThofGWRQ5m91R/view?usp=sharing) en vigueur en RDC . Pour avoir le nombre des retournées, elle recommande que soient considérés les données mouvements retour ayant été évaluées sur 2 ans en se référant à la date d’évaluation « evaluation_date» et ceci avec comme référence le mois de l’extraction mais également, en prenant unique les mouvements ayant enregistrés les 18 derniers mois en se basant sur la date mouvement « movement_date » . Les colonnes « person » et « household » reprennent respectivement le nombre des personnes et ceux des ménages déplacés. Ces filtres sont applique sur la base de données globale.

Tags: displacement, hxl, internally displaced persons-idp, returnees
Organization: OCHA Democratic Republic of the Congo (DRC)
IGAD Region - Monthly Combined Drought Indicator (CDI) 2026

Monthly Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

Tags: climate hazards, climate-weather, drought, hazards and risk
Organization: IGAD Climate Prediction and Applications Center (ICPAC)
IGAD Region - Dekadal Combined Drought Indicator (CDI) 2026

Dekadal (10 days) Combined Drought Indicator (CDI) as implemented in the East Africa Drought Watch, and which is used for detecting and monitoring areas that either are affected or have the potential to be affected by meteorological, agricultural and/or hydrological drought.

Tags: climate hazards, climate-weather, drought, hazards and risk
Organization: IGAD Climate Prediction and Applications Center (ICPAC)
Yemen - Subnational Administrative Boundaries

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](https://www.geoboundaries.org) is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.  

Tags: administrative boundaries-divisions, gazetteer, geodata
Organization: geoBoundaries
Satellite detected water extent over Sylhet, Mymensingh, Dhaka,  and Chattogram Divisions, Bangladesh  as of 28 May 2022

**UNOSAT code: FL20220525BGD**  This map illustrates satellite-detected surface waters in Sylhet, Mymensingh, Dhaka, and Chattogram Divisions, Bangladesh as observed from a Sentinel-1 images acquired on 28 May 2022 at 18:04 local time and using an automated analysis with machine learning method. Within the analyzed area of about 12,000 km2, about 4,500 km2 of lands appear to be flooded. In this area, about 3,400 km2 of croplands and 1,000 km2 of herbaceous wetland appear to be likely affected by the flood waters. 

Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in the district of Sunamganj with ~935,000 people and Kishoreganj with ~779,000 people.

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.

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Madagascar - Subnational Administrative Boundaries

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3, ADM4.  
  
Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database [www.geoboundaries.org](https://www.geoboundaries.org) is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.  

Tags: administrative boundaries-divisions, gazetteer, geodata
Organization: geoBoundaries
Geodata of Damage Assessment of Sana'a International Airport, Sana'a Governorate, Yemen

This map illustrates satellite-detected damage and destruction at Sana'a International Airport, Sana'a Governorate, Yemen. Using satellite imagery acquired 15 May 2015 and 12 December 2014, UNITAR-UNOSAT identified a total of 70 affected structures and transportation vehicles. Approximately 18 of these were destroyed, 32 severely damaged, and 20 moderately damaged. Additionally, 32 impact craters were found. One medical facility was identified within 500 meters of impact craters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

Tags: complex emergency-conflict-security, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Rwanda - Subnational Administrative Boundaries

This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3, ADM4, ADM5.  
  
Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database [www.geoboundaries.org](https://www.geoboundaries.org) is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.  

Tags: administrative boundaries-divisions, gazetteer, geodata
Organization: geoBoundaries
Colombia: Estimaciones Afectaciones por Fenómenos Hidrometeorológicos Segundo

Con base en los escenarios de predicción del Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), se identificaron las zonas con susceptibilidad a inundaciones y deslizamientos durante el segudno trimestre de 2026. El archivo de Excel que acompaña esta publicación contiene ocho hojas con información desagregada sobre personas, porcentaje de población, zonas de cultivos, instituciones educativas y centros de salud localizados en zonas susceptibles. Además, para cada municipio se incluye el nivel de riesgo estimado, calculado mediante el Índice Municipal de Riesgo Ajustado por Capacidades, desarrollado por el Departamento Nacional de Planeación (DNP).


Organization: 3iS
Nauru: IBTrACS Storm Tracks

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.

Tags: cyclones-hurricanes-typhoons
Organization: HDX