AndreaBozzo/ceres-open-data-index huggingface dataset:
Satellite Detected Water Extent in Khamkeuth District, Borikhamxay Province, Lao PDR

This map illustrates the satellite-detected water extent in the district of Khamkeuth in Borikhamxay province, in the central-northern 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 11 August 2017 and detected several areas with potentially standing waters. In the district of Khamkeuth ~2,441 ha are likely flooded. Kindly note, the district of Khamkeuth has been partially analyzed due to the image does not cover the full district. 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)
Ukraine: 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
Cambodia: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Cambodia: (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
Haiti: High Resolution Population Density Maps + Demographic Estimates

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Haiti: (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
Australia: 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
Pakistan: 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
State of Palestine: 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
Estonia: 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
Colombia: 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
United Kingdom: 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
Damage assessment in Siargao Island, General Luna, Surigao del Norte of Philippines as of 26 December 2021

**UNOSAT code: TC20211216PHL**  This map illustrates potentially damaged structures/buildings in Siargao Island, General Luna, Surigao del Norte of Philippines as detected by Pleiades image acquired on 26 December 2021 and WorldView-3 acquired on 23 December 2021. Within the analyzed area, UNOSAT has identified 1,769 damaged structures, 380 potentially damaged structures.
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: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Ethiopia: Seasonal Temperature Condition Index

These datasets provide Ethiopia’s Temperature Condition Index (TCI) values for the past five years, focusing specifically on the growing season (June–September). The Temperature Condition Index is a remote sensing-based indicator used to evaluate vegetation stress resulting from temperature extremes, primarily heat stress. TCI is calculated using Land Surface Temperature (LST) data sourced from the MODIS platform.
Higher TCI values reflect cooler and more favorable conditions for plant growth, while lower values are associated with increased heat stress and potential vegetation damage. For long-term seasonal analysis, MODIS LST data from 2000 to the present were utilized, with an emphasis on the June–September period. These time series datasets highlight TCI values from the recent five growing seasons, enabling direct comparison with current year conditions.


Tags: drought, food security
Organization: 3iS
Ethiopia: Seasonal Vegetation Condition Index

These datasets provide Ethiopia’s Vegetation Condition Index (VCI) values for the past five years, specifically covering the June to September growing season. The VCI is a satellite-derived indicator used to evaluate vegetation health and drought conditions. It distinguishes drought-related stress from other factors affecting vegetation growth. The VCI values range from 0 to 100, where 0% indicates the worst vegetation condition—approaching the historical minimum and signifying severe drought—and 100% indicates optimal conditions, near the historical maximum, reflecting healthy and lush vegetation.
The VCI data is obtained from the MODIS satellite. The long-term dataset used for seasonal analysis spans from the year 2000 to the present, focusing on the months of June through September, aligning with Ethiopia’s main growing season. These time series datasets enable the comparison of current vegetation conditions with those of recent growing seasons, supporting drought monitoring and vegetation health assessments.


Organization: 3iS
Ethiopia: Seasonal Vegetation Health Index (June, July, August, & September)

These datasets provide Ethiopia’s Vegetation Health Index (VHI) values for the past five years, focusing specifically on the growing season from June to September. The Vegetation Health Index (VHI) is a satellite-derived indicator used to monitor the overall health and stress conditions of vegetation, particularly in relation to drought, heat, and moisture availability. It offers an integrated measure by combining data on vegetation greenness and thermal conditions.
VHI is a vital remote sensing index for assessing vegetation condition, drought impacts, and agricultural stress. It combines two indices: the Vegetation Condition Index (VCI), which reflects vegetation greenness, biomass, and vigor; and the Temperature Condition Index (TCI), which indicates thermal conditions including heat and water stress.
The long-term dataset analyzed for seasonal VHI spans from 2000 to the present, concentrating on the months of June, July, August, and September, corresponding to the Ethiopian growing season. These time series datasets highlight VHI values for the most recent five growing seasons, enabling direct comparison with current year conditions.


Organization: 3iS
Tuvalu - Human Development Indicators

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Tags: demographics, development, education, gender, health, indicators, socioeconomics
Organization: UNDP Human Development Reports Office (HDRO)
State of Palestine - Human Development Indicators

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Tags: demographics, development, education, gender, health, indicators, socioeconomics
Organization: UNDP Human Development Reports Office (HDRO)