AndreaBozzo/ceres-open-data-index huggingface dataset:
Flooding in the Philippines on November 09, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 09, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
Flooding in the Philippines on November 06, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 06, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
Flooding in the Philippines on November 11, 2025

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 11, 2025, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

Tags: affected area, climate hazards, cyclones-hurricanes-typhoons, damage assessment, disaster risk reduction-drr, flooding, geodata, natural disasters
Organization: The Philippine Space Agency
British Indian Ocean Territory: WOF Administrative Subdivisions and Human Settlements

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:  
- macroregion (admin-1 including region)  
- region (admin-2 including state, province, department, governorate)  
- macrocounty (admin-3 including arrondissement)  
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)  
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)  
  
The dataset also contains human settlement points and polygons for:  
- localities (city, town, and village)  
- neighbourhoods (borough, macrohood, neighbourhood, microhood)  
  
The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the [README.md](https://github.com/whosonfirst/whosonfirst-sources/blob/main/sources/README.md) file for complete data source metadata. Refer to our [blog post](https://whosonfirst.org/blog/2023/05/31/shapefiles/) for explanation of field names.  
  
Data corrections can be proposed using [Write Field](https://writefield.nextzen.org/), an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

Tags: administrative boundaries-divisions, geodata, populated places-settlements, population
Organization: Who's On First
Bangladesh: High Resolution Population Density Maps + Demographic Estimates

VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Bangladesh : (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Rapidly assessed damage occurring between 3 December 2017 and 23 February 2018 in Eastern Ghouta Area, Syria

This map illustrates satellite-detected damage in the subdistricts of Kafr Batna and Irbin and in the eastern part of Damascus city, Syrian Arab Republic. Using satellite imagery collected 23 February 2018 and comparing with imagery acquired 3 December 2017, UNITAR - UNOSAT conducted a Rapid Damage Assessment, over a total area of 62.5 square kilometers, to provide an overview of areas of recent damage. The area analyzed was divided in cells and each cell was assessed searching for presence of new damage. Our analysis shows that 29% of the cells were affected by major new damage, with presence of buildings completely destroyed or severely damaged between 3 December 2017 and 23 February 2018. In addition 24% of the cells showed signs of minor new damage, with visible impact craters, debris or moderately damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.

Tags: geodata
Organization: United Nations Satellite Centre (UNOSAT)
Syria - Damage assessement in Hama, Hama Governorate.

This map illustrates satellite-detected damage and destruction in a portion of the city of Hama, Hama Governorate, Syria. Using satellite imagery acquired 06 July and 30 June 2016, 05 March 2014, 26 September 2013, and 06 August 2010, UNITAR - UNOSAT identified a total of 5,968 affected structures, of which 4,969 were destroyed, 345 severely damaged, and 654 moderately damaged. This analysis does not include pre-war military bases and facilities. This analysis was done as part of the REACH initiative for the U.S. Office of Foreign Disaster Assistance. 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)
Satellite Detected Water Extent in Khongxedone District, Salavan Province, Lao PDR

This map illustrates the satellite-detected water extent in the District of Khongxedone, Salavan Province, in the southwestern part of Lao People’s Democratic Republic after the tropical storm SONCA-17. The UNITAR-UNOSAT analysis used a Sentinel-1 satellite image acquired on the 30 July 2017 and detected several areas with potentially standing waters. In the district of Khongxedone ~2,921 ha are likely flooded mostly affecting the central part of the district along the “Xe Don” river. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Belize: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Belize: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Satellite detected water extent over Sudurpaschim and Lumbini provinces, Nepal as of 4 July 2021

**UNOSAT code: FL20210630NPL**  This map illustrates satellite-detected surface waters in Sudurpaschim and Lumbini provinces, Nepal as observed from a Sentinel-1 image acquired on 4 July 2021 at 18:38 local time and using an automated analysis with Machine learning method.  Within the analyzed area of 7,055 km2, about 170 km2 of lands appear to be flooded. The water extent appears to have increased of about 150 km2 since 30 June 2021. 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 Kailali with ~28,000 people, Banke with ~23,000 people, and Bardiya with ~21,000 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)
Geodata of Snow cover extent over West Bank, Israel, Jordan, Lebanon and Syria

This map illustrates the extent of the snow cover caused by the storm "Huda" that moved through the Middle East region striking Lebanon, Jordan, and the West Bank from about 7-13 January. This analysis was based on satellite imagery collected by the MODIS sensor on the NASA AQUA satellite on 13 January 2015. As seen in the imagery the snow has covered a very extensive part of the Lebanon and Syria. A decrease in the snow cover extent is observed in certain areas in Southern Lebanon and Syria compared to previous UNOSAT analysis with imagery collected on 12 January. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected waters in Alta Verapaz, Izabal and Zacapa departments of Guatemala as of 18 November 2020

**UNOSAT code: TC20201119GTM**  This map illustrates satellite-detected surface waters in Alta Verapaz, Izabal and Zacapa departments of Guatemala as observed from a Sentinel-1 image acquired on 18 November 2020 at 18:06 local time. Within the analyzed area of about 15,000 km2, a total of about 170 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 17,500 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)
Barbados: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Barbados: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Tunisia: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Tunisia: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Tags: baseline population, children, elderly, geodata, women, youth
Organization: AI for Good at Meta
Central America - Prevalence of Moderate or Severe Food Insecurity

Prevalence of moderate or severe food insecurity has been assessed based on the information obtained from the FAO (Food Agriculture Organization of the United Nations). The hazard classification is determined by the frequency of the events. The INSAN is an indicator that measures the percentage of people who have experienced moderate or severe food insecurity. Comparisons are made by region: the world, Central America, and the countries of El Salvador, Guatemala, and Honduras. Categories such as urbanization (rural, urban, and peri-urban), sex (female, male), and total are included.

For more information contact GIS4Tech: info@gis4tech.com. You can also visit the PREDISAN platform https://predisan.gis4tech.com/ca4 for detailed, accurate information.

Tags: food security
Organization: Acción contra el hambre - GIS4tech
Costa Rica: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Costa Rica: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Latin America - Flash Environmental Assessment Tool (FEAT) Tables

This tables presents results from the Flash Environmental Assessment Tool assessments conducted in 12 countries in Latin America in Q4 2021 and Q1 2022.

The FEAT helps to identify potential hazards posed by hazardous operations. The FEAT is used at the onset of hazardous operations or disasters, by international humanitarian responders that may be unfamiliar with the affected area.

The FEAT was initially developed based on lessons stemming from the 2004 Indian Ocean Earthquake and Tsunami, with the aim to provide a standardized, scientific assessment methodology to prioritize the impacts of chemical accidents following large scale, sudden onset natural disasters.

The outcome of the FEAT is the identification of impact zones (areas most at risk from hazardous chemical releases). These zones highlight locations where urgent response actions are needed to protect human health and the environment.

[More information on the FEAT Tool](https://www.unocha.org/publications/report/world/flash-environmental-assessment-tool-feat-20-pocket-guide)

Tags: affected area, affected population, environment
Organization: UNEP/OCHA Joint Environment Unit
Monthly Oceanic Nino Index (ONI)

The Oceanic Niño Index (ONI) has become the de facto standard that the National
Oceanic and Atmospheric Administration (NOAA) uses to identify El Niño (warm) and
La Niña (cool) events in the tropical Pacific. It is the three month mean SST
anomaly for the El Niño 3.4 region (i.e., 5°N-5°S, 120°-170°W). Events are defined as five
consecutive overlapping three month periods at or above the +0.5°C anomaly for warm (El
Niño), events and at or below the -0.5 anomaly for cold (La Niña) events. The threshold
is further broken down into Weak (with a 0.5 to 0.9 SST anomaly), Moderate (1.0 to 1.4)
and Strong (≥ 1.5) events. For an event to be categorized as weak, moderate or strong. it
must have equalled or exceeded the threshold for at least three consecutive overlapping
three month periods.

Tags: climate-weather, el nino-el nina
Organization: HDX
Bhutan: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Bhutan: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Tags: children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Subnational INFORM - Caucasus and Central Asia

The subnational INFORM model for Caucasus and Central Asia was initiated by the Regional Inter-Agency Standing Committee (IASC) Task Force for Caucasus and Central Asia and is managed by OCHA. The INFORM model is being used to support coordinated preparedness actions. Partners hope to use the model to improve cooperation between humanitarian and development actors in managing risk and building resilience across the region.

INFORM identifies areas at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM model is based on risk concepts published in scientific literature and envisages three dimensions of risk: Hazards & Exposure, Vulnerability and Lack of Coping Capacity. The INFORM model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk.

The regional subnational INFORM model for Caucasus and Central Asia is developed at the first administrative level (corresponding to the provinces/oblasts/regions and few independent cities) of the eight countries in South Caucasus and Central Asia.

The INFORM index supports a proactive crisis management framework. It will be helpful for an objective allocation of resources for disaster management as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies.

Tags: geodata, hazards and risk
Organization: OCHA ROCCA (inactive)
Kyrgyzstan - Population Census 2009

This dataset contains population census data from 2009 for Kyrgyzstan.  It has total population dissagregated by gender and urban/rural area at country level and first and second administrative levels. It includes the number of households for these administrative areas and the average household sizes.

The data was received through UNFPA from the National Statistical Committee of the Kyrgyz Republic.

Tags: baseline population, census, demographics, gender
Organization: OCHA ROCCA (inactive)
Cayman Islands: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in the Cayman Islands: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Tags: baseline population, children, elderly, geodata, health, women, youth
Organization: AI for Good at Meta
Seychelles - Subnational Administrative Boundaries

Seychelles administrative level 0-3 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.
This COD-AB has NOT been reviewed for accuracy and necessary changes in the last 12 months.

Sourced from Seychelles National Bureau of Statistics

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Seychelles [COD-PS](https://data.humdata.org/dataset/cod-ps-syc).

As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required.

Please see the [COD Portal](https://cod.unocha.org/).

Administrative level 1 contains 4 feature(s).  The normal administrative level 1 feature type is "currently not known".

Administrative level 2 contains 8 feature(s).  The normal administrative level 2 feature type is "currently not known".

Administrative level 3 contains 27 feature(s).  The normal administrative level 3 feature type is "currently not known".

Recommended cartographic projection:  Africa Albers Equal Area Conic

This metadata was last updated on January 9, 2025.

Tags: administrative boundaries-divisions, gazetteer
Organization: OCHA Regional Office for Southern and Eastern Africa (ROSEA)
Georgia - Number of people living with disabilities by region

According to information by the Ministry of Labour, Health and Social Affairs , 118 651 persons with disabilities are registered as recipients of state social assistance by 1 March, 2015 in Georgia that constitutes 3 percent of total population resided in Georgia.

This dataset provides a breakdown of the number of persons with disabilities by first administrative level (region), and a detailed breakdown for the districts belonging to the capital city of Tbilisi.

The provided information depicts the number of disabled persons receiving state social pension/allowance (beneficiaries) across the country. In light of this, state policy determines the total number of disabled persons by the sum of beneficiaries, which directly is connected to the actual number of disabled people living in Georgia. The actual number of disabled persons in Georgia is likely to be higher.

Tags: baseline population, demographics, disability, population
Organization: OCHA ROCCA (inactive)
Lesotho - Subnational Administrative Boundaries

Lesotho administrative level 0-2 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE:  P-codes contain alphabetical ADM1 designator.
This COD-AB has NOT been reviewed for accuracy and necessary changes in the last 12 months.

Sourced from FAO and the Ministry of Local Government 2016

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Lesotho [COD-PS](https://data.humdata.org/dataset/cod-ps-lso).

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the [COD Portal](https://cod.unocha.org/).

Administrative level 1 contains 10 feature(s).  The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 78 feature(s).  The normal administrative level 2 feature type is ""currently not known"".

Recommended cartographic projection:  Africa Albers Equal Area Conic

This metadata was last updated on January 9, 2025.

Tags: administrative boundaries-divisions, gazetteer, geodata
Organization: OCHA Regional Office for Southern and Eastern Africa (ROSEA)
Mauritius - Subnational Administrative Boundaries

Mauritius administrative level 0-1 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE:  COD-AB contains uninhabited features not represented in COD-PS.
This COD-AB has NOT been reviewed for accuracy and necessary changes in the last 12 months.

Sourced from Statistics Mauritius

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Mauritius [COD-PS](https://data.humdata.org/dataset/cod-ps-mus).

As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required.

Please see the [COD Portal](https://cod.unocha.org/).

Administrative level 1 contains 12 feature(s).  The normal administrative level 1 feature type is ""currently not known"".

Recommended cartographic projection:  Africa Albers Equal Area Conic

This metadata was last updated on January 9, 2025.

Tags: administrative boundaries-divisions, gazetteer
Organization: OCHA Latin America and the Caribbean (ROLAC)
Rwanda - Subnational Administrative Boundaries

Rwanda administrative level 0-4 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE:  COD-AB extends to ADM4.  COD-PS extends to ADM3.
This COD-AB has NOT been reviewed for accuracy and necessary changes in the last 12 months.

Sourced from National Institute of Statistics of Rwanda (NISR).

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Rwanda [COD-PS](https://data.humdata.org/dataset/cod-ps-rwa).

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the [COD Portal](https://cod.unocha.org/).

Administrative level 1 contains 5 feature(s).  The normal administrative level 1 feature type is ""currently not known"".

Administrative level 2 contains 30 feature(s).  The normal administrative level 2 feature type is ""currently not known"".

Administrative level 3 contains 416 feature(s).  The normal administrative level 3 feature type is ""currently not known"".

Administrative level 4 contains 1,000 feature(s).  The normal administrative level 4 feature type is ""currently not known"".

Recommended cartographic projection:  Africa Albers Equal Area Conic

This metadata was last updated on January 9, 2025.

Tags: administrative boundaries-divisions, gazetteer, geodata
Organization: OCHA Regional Office for Southern and Eastern Africa (ROSEA)