AndreaBozzo/ceres-open-data-index huggingface dataset:
Satellite detected water extents in Northeastern part of Thailand

**UNOSAT code: FL20210928THA**  This map illustrates satellite-detected surface waters in Khon Kaen, Chaiyaphum, Nakhon Ratchasima, and Nong Bua Lam Phu provinces, Thailand as observed from a Sentinel-1 image acquired on 5 Oct 2021 at 18:20 local time and using an automated analysis with Machine learning method. 
Within the analyzed area of about 40,500 km2 , about 770 km2 of lands appear to be flooded. The water extent appears to have decreased of about 70 km2 since 29 September 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 Phimai with ~ 10,400 people, Non Sung with ~ 9,000 people, Mueang Chaiyaphum with ~ 6,700 people, Mueang Khon Kaen with ~ 4,500 people, and Chonnabot Thai with ~ 4,300 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)
Ethiopia - Addis Ababa subcities and woredas

The dataset titled "Addis Ababa City Administration Woreda Boundary" provides detailed geographic information on the administrative divisions within Addis Ababa, Ethiopia. It includes shapefiles delineating the boundaries of both subcities and woredas (districts) within the city. Each feature in the dataset is attributed with identifiers and names corresponding to specific subcities and woredas, facilitating spatial analysis and mapping of Addis Ababa's administrative structure.​

This dataset is particularly useful for urban planning, resource allocation, and various analyses requiring precise administrative boundary data within Addis Ababa.

Original source: [Addis Ababa City Administration Woreda Boundary
](https://data.moa.gov.et/dataset/addis-ababa-city-administration-woreda-boundary)

Tags: administrative boundaries-divisions
Organization: 3iS
Syria - Damage density 2015 of Daraa, Daraa Governorate,

This map illustrates satellite-detected damage density in the city of Daraa, Syrian Arab Republic. Using satellite imagery acquired 04 June 2015, 01 May 2014, 07 September 2013, and 14 December 2010, UNITAR - UNOSAT identified a total of 990 affected structures, of which 152 were destroyed, 358 severely damaged, and 480 moderately damaged. This analysis does not include pre-war military bases and facilities. Trend analysis shows an increase in the number of damaged structures since 1 May 2014 and approximately 618 structures are newly damaged. 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)
Ethiopia: Humanitarian Needs

This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.

Tags: humanitarian needs overview-hno, hxl, people in need-pin
Organization: OCHA Humanitarian Programme Cycle Tools (HPC Tools)
Projected Daily Maximum Temperature Extremes by Country: SSP585 2050 Scenario (CHC-CMIP6)

This climate projection dataset contains global, daily gridded data for the SSP585 2050 scenario to be used in the identification and monitoring of hydroclimatic extremes.

The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme heat conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product form the basis of the 1983–2016 historical record. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 temperature projections. Finally, monthly counts of frequency of extremes for each variable were derived for each time period.

Two scenarios were used from CMIP6—Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.537. The SSP245 scenario is based on ‘middle-of-the-road’ projections of development (SSP2). The SSP585 scenario projects rapid fossil fuel development and increased global market integration (SSP5). These are generally considered the most-likely scenario (SSP245) and the high-emissions scenario (SSP585)

Given the two projection periods 2025–2035 and 2045–2055, projections for four CMIP6 scenarios (2030_SSP245, 2030_SSP585, 2050_SSP245, 2050_SSP585) were derived. Counts of the number of extreme days per month were calculated for Tmax for the four scenarios. Definitions of extremes for each variable were based on two methods: known thresholds (30°C and 40.6°C) and by calculating pixel-specific breakpoints using the 95th and 99th percentile.

30°C and 40.6°C represent moderate and extreme heat exposure. These were chosen based on documented thresholds for agricultural and human heat stress. For each variable, year, and scenario, the number of days surpassing each variables’ thresholds were calculated.

For Tmax for each pixel, the daily 95th and 99th percentiles were calculated using 1983–2016 daily data, resulting in a 95th and 99th percentile value for each variable. For each of these variables, each year (1983–2016), and each of the four scenarios, the number of days for each month were calculated at each pixel that surpass these percentile-defined extreme values.

More information can be found in this [article in the Nature journal](https://www.nature.com/articles/s41597-024-03074-w) and and in this [technical documentation](https://data.chc.ucsb.edu/products/CHC_CMIP6/Data_Descriptor_CHC_CMIP6_climate_projection_dataset.pdf).


Tags: climate-weather, environment
Organization: Climate Hazards Center
Projected Daily Maximum Temperature Extremes by Country: SSP585 2030 Scenario (CHC-CMIP6)

This climate projection dataset contains global, daily gridded data for the SSP585 2030 scenario to be used in the identification and monitoring of hydroclimatic extremes.

The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme heat conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product form the basis of the 1983–2016 historical record. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 temperature projections. Finally, monthly counts of frequency of extremes for each variable were derived for each time period.

Two scenarios were used from CMIP6—Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.537. The SSP245 scenario is based on ‘middle-of-the-road’ projections of development (SSP2). The SSP585 scenario projects rapid fossil fuel development and increased global market integration (SSP5). These are generally considered the most-likely scenario (SSP245) and the high-emissions scenario (SSP585)

Given the two projection periods 2025–2035 and 2045–2055, projections for four CMIP6 scenarios (2030_SSP245, 2030_SSP585, 2050_SSP245, 2050_SSP585) were derived. Counts of the number of extreme days per month were calculated for Tmax for the four scenarios. Definitions of extremes for each variable were based on two methods: known thresholds (30°C and 40.6°C) and by calculating pixel-specific breakpoints using the 95th and 99th percentile.

30°C and 40.6°C represent moderate and extreme heat exposure. These were chosen based on documented thresholds for agricultural and human heat stress. For each variable, year, and scenario, the number of days surpassing each variables’ thresholds were calculated.

For Tmax for each pixel, the daily 95th and 99th percentiles were calculated using 1983–2016 daily data, resulting in a 95th and 99th percentile value for each variable. For each of these variables, each year (1983–2016), and each of the four scenarios, the number of days for each month were calculated at each pixel that surpass these percentile-defined extreme values.

More information can be found in this [article in the Nature journal](https://www.nature.com/articles/s41597-024-03074-w) and and in this [technical documentation](https://data.chc.ucsb.edu/products/CHC_CMIP6/Data_Descriptor_CHC_CMIP6_climate_projection_dataset.pdf).


Tags: climate-weather, environment
Organization: Climate Hazards Center
Projected Daily Maximum Temperature Extremes by Country: SSP245 2050 Scenario (CHC-CMIP6)

This climate projection dataset contains global, daily gridded data for the SSP245 2050 scenario to be used in the identification and monitoring of hydroclimatic extremes.

The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme heat conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product form the basis of the 1983–2016 historical record. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 temperature projections. Finally, monthly counts of frequency of extremes for each variable were derived for each time period.

Two scenarios were used from CMIP6—Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.537. The SSP245 scenario is based on ‘middle-of-the-road’ projections of development (SSP2). The SSP585 scenario projects rapid fossil fuel development and increased global market integration (SSP5). These are generally considered the most-likely scenario (SSP245) and the high-emissions scenario (SSP585)

Given the two projection periods 2025–2035 and 2045–2055, projections for four CMIP6 scenarios (2030_SSP245, 2030_SSP585, 2050_SSP245, 2050_SSP585) were derived. Counts of the number of extreme days per month were calculated for Tmax for the four scenarios. Definitions of extremes for each variable were based on two methods: known thresholds (30°C and 40.6°C) and by calculating pixel-specific breakpoints using the 95th and 99th percentile.

30°C and 40.6°C represent moderate and extreme heat exposure. These were chosen based on documented thresholds for agricultural and human heat stress. For each variable, year, and scenario, the number of days surpassing each variables’ thresholds were calculated.

For Tmax for each pixel, the daily 95th and 99th percentiles were calculated using 1983–2016 daily data, resulting in a 95th and 99th percentile value for each variable. For each of these variables, each year (1983–2016), and each of the four scenarios, the number of days for each month were calculated at each pixel that surpass these percentile-defined extreme values.

More information can be found in this [article in the Nature journal](https://www.nature.com/articles/s41597-024-03074-w) and and in this [technical documentation](https://data.chc.ucsb.edu/products/CHC_CMIP6/Data_Descriptor_CHC_CMIP6_climate_projection_dataset.pdf).


Tags: climate-weather, environment
Organization: Climate Hazards Center
Projected Daily Maximum Temperature Extremes by Country: SSP245 2030 Scenario (CHC-CMIP6)

This climate projection dataset contains global, daily gridded data for the SSP245 2030 scenario to be used in the identification and monitoring of hydroclimatic extremes.

The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme heat conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product form the basis of the 1983–2016 historical record. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 temperature projections. Finally, monthly counts of frequency of extremes for each variable were derived for each time period.

Two scenarios were used from CMIP6—Shared Socioeconomic Pathway (SSP) 2–4.5 and 5–8.537. The SSP245 scenario is based on ‘middle-of-the-road’ projections of development (SSP2). The SSP585 scenario projects rapid fossil fuel development and increased global market integration (SSP5). These are generally considered the most-likely scenario (SSP245) and the high-emissions scenario (SSP585)

Given the two projection periods 2025–2035 and 2045–2055, projections for four CMIP6 scenarios (2030_SSP245, 2030_SSP585, 2050_SSP245, 2050_SSP585) were derived. Counts of the number of extreme days per month were calculated for Tmax for the four scenarios. Definitions of extremes for each variable were based on two methods: known thresholds (30°C and 40.6°C) and by calculating pixel-specific breakpoints using the 95th and 99th percentile.

30°C and 40.6°C represent moderate and extreme heat exposure. These were chosen based on documented thresholds for agricultural and human heat stress. For each variable, year, and scenario, the number of days surpassing each variables’ thresholds were calculated.

For Tmax for each pixel, the daily 95th and 99th percentiles were calculated using 1983–2016 daily data, resulting in a 95th and 99th percentile value for each variable. For each of these variables, each year (1983–2016), and each of the four scenarios, the number of days for each month were calculated at each pixel that surpass these percentile-defined extreme values.

More information can be found in this [article in the Nature journal](https://www.nature.com/articles/s41597-024-03074-w) and and in this [technical documentation](https://data.chc.ucsb.edu/products/CHC_CMIP6/Data_Descriptor_CHC_CMIP6_climate_projection_dataset.pdf).


Tags: climate-weather, environment
Organization: Climate Hazards Center
Satellite detected waters in Quang Nam Province of Viet Nam as of 20 October 2020

**UNOSAT code: TC20201013VNM**  This map illustrates satellite-detected surface waters in Quang Nam Province of Viet Nam as observed from a Kompsat-5 image acquired on 20 October 2020. Within the analyzed area of about 4,000 km2, a total of about 170 km2 of lands appear to be flooded. The water extent appears to have increased of about 40 km2 since 17 October 2020. Based on Worldpop population data and the detected surface waters, about 90,000 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)
Bottom-up gridded population estimates for Nigeria, version 2.0

This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100 m grid cells) with national coverage for Nigeria, along with estimates of the number of people belonging to various age-sex groups. Version 2.0 is an update to the previous version 1.2 gridded population estimates and is based on more recent and detailed settlement information and a different regional boundary definition. These model-based population estimates most likely represent the time period around 2019, corresponding to the period when the satellite imagery was processed to generate building footprints. Populations are mapped only into areas where residential settlements are predicted.

These data were produced by the [WorldPop](https://www.worldpop.org/) Research Group at the University of Southampton in collaboration with the National Population Commission of Nigeria. This work was part of the Geo-Referenced Infrastructure and Demographic Data for Development ([GRID3 ](https://grid3.org/)) programme with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Foreign, Commonwealth & Development Office (INV 009579, formerly OPP 1182425). Project partners included the United Nations Population Fund, Center for International Earth Science Information Network ([CIESIN](http://www.ciesin.org/)), a center within the Columbia Climate School at Columbia University, and the [Flowminder](https://www.flowminder.org/) Foundation. Statistical modelling was led by Chris Jochem and Doug Leasure additional support and oversight from Attila Lazar and Andy Tatem. Chris Lloyd provided the residential building classification. The microcensus data were originally collected by eHealth Africa and Oak Ridge National Laboratory with support from the Bill and Melinda Gates Foundation. The WorldPop grou  and GRID3 partners are acknowledged for their project support.

__RELEASE CONTENT__

1.   NGA_population_v2_0_gridded.zip
2.   NGA_population_v2_0_admin.zip
3.   NGA_population_v2_0_sql.sql
4.   NGA_population_v2_0_mastergrid.tif
5.   NGA_population_v2_0_tiles.zip
6.   NGA_population_v2_0_agesex.zip

__LICENSE__
These data (1-6) may be redistributed using a [Creative Commons Attribution 4.0 International (CC BY 4.0) ](https://creativecommons.org/licenses/by/4.0/) License. 

__Recommended citations__

_WorldPop and National Population Commission of Nigeria. 2021. Bottom-up gridded population estimates for Nigeria, version 2.0. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00729._


For further details, please, read NGA_population_v2_0_README.pdf




Tags: baseline population, demographics, geodata
Organization: GRID3
AFD Development Assistance

### Development assistance data from [AFD - French Development Agency](http://opendata.afd.fr) 
The dataset covers French development assistance, on ongoing projects in 2015. The data are disclose as receivers agreed France to share it. AFD aims at updating the data every trimester to take into account the flow of new project being funded.
Cumulative fundings were update December the 30th 2015 and respect  IATI standards.

---

### Données de l'aide au développement de l' [AFD - Agence Française de Développement](http://opendata.afd.fr) 
Les données portent sur l’aide au développement française sur les projets réalisés en souverain et en cours d’exécution en 2015. Ces données peuvent être publiées dès lors que l'accord de la contrepartie a été obtenu.L'Agence Française de Développement visera une actualisation trimestrielle de la publication de ces données, notamment pour prendre en compte les nouveaux projets de développement financés par l’Agence. Il est à noter que les montants cumulés des versements ont été mis à jour le 30 décembre 2015. Ces données respectent le standard IATI (Initiative internationale pour la transparence de l’aide).


Tags: funding
Organization: Agence Française de Developpement (AFD) (inactive)
Gridded disaggregated population estimates for Kenya , version 2.0

These data were produced by the [ WorldPop](https://www.worldpop.org/)  Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund ([ UNICEF ](https://www.unicef.org/)) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of  Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work. In-country engagements were done by David Kyalo, Olena Borkovska ([ GRID3 ](https://grid3.org/ Inc), Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and gender and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem.

**Recommended citations**

Gadiaga A. N., Abbott T. J., Chamberlain H., Lazar A. N., Darin E., Tatem A. J. 2023. Census disaggregated gridded population estimates for Kenya (2022), version 2.0. University of Southampton. doi:10.5258/SOTON/WP00762

**License**

*These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information*.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org.

[WorldPop](https://www.worldpop.org/), University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.


Tags: demographics, population
Organization: WorldPop