AndreaBozzo/ceres-open-data-index huggingface dataset:
Geodata of Damage Assessment of Tuhure Pasal Area, Western Region, Nepal

This map illustrates satellite-detected damage and destruction in the Tuhure Pasal area of Nepal. Located roughly 27 to 31 kilometers southwest of the 25 April 2015 earthquake epicenter, the Tuhure Pasal area was impacted by this event. Using satellite imagery acquired 03 May 2015 UNITAR / UNOSAT identified a total of 86 affected structures in the Tuhure Pasal area. Approximately 32 of these were destroyed, 21 severely damaged, and 33 moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.

Tags: earthquake-tsunami, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Gambia: 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, hxl
Organization: HDX
Guinea: 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, hxl
Organization: HDX
Western Sahara: 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, hxl
Organization: HDX
Côte d'Ivoire: 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, hxl
Organization: HDX
Burkina Faso: 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, hxl
Organization: HDX
Ukraine Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, hxl, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
Nicaragua Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, hxl, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
Republic of Moldova Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, hxl, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
Saint Lucia Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, hxl, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
Barbados Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, hxl, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
Bosnia and Herzegovina Multidimensional Poverty Index

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).


Tags: development, education, health, hxl, indicators, mortality, nutrition, poverty, socioeconomics, sustainable development goals-sdg, water sanitation and hygiene-wash
Organization: Oxford Poverty & Human Development Initiative
Damage assessment of Old City, Mosul, Ninawa Governorate, Iraq (18 July 2017)

This map illustrates satellite-detected damage in Old City, Mosul, Ninawa Governorate, Iraq. Using satellite imagery acquired 18 July 2017, UNITAR - UNOSAT identified a total of 6,981 affected structures within this part of city. This marks an overall increase of 26% in damage affected structures from the previous assessment of the 30 June. Approximately 1,202 of these were destroyed (17% of the total affected buildings), 3,982 severely damaged (57% of the total affected buildings) and 1,797 moderately damaged (26% of the total affected buildings). This marks a 145% increase in destroyed buildings, 4% in moderately damaged buildings and 20% severely damaged buildings from the 30 June assessment. The most heavily impacted areas appear to be the Ras al-Kur (see inset) and the Maydan neighbourhoods. Due to the densely constructed nature of this part of the city, these values might be underestimated. 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 Sindh province, Pakistan, as of 15 August 2022

**UNOSAT code: FL20220808PAK**  This map illustrates satellite-detected surface waters in Sindh province, Pakistan as observed from a Sentinel-1 image acquired on 15 August 2022 at 18:36 local time. Within the analyzed area of about 30,000 km2, about 1,550 km2 of lands appear to be flooded. Water extent appears to have decreased of about 325 km2 since the 06 August 2022. within the analyzed area. Based on Worldpop population data and the detected surface waters in the analyzed area, about 410,000 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 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 Gendrasa Refugee Camp, Maban County, Upper Nile State, South Sudan

This map illustrates satellite-detected shelters and other buildings at the Gendrasa refugee camp in Maban County, Upper Nile State, South Sudan. As of 08 April 2015 a total of 9,692 shelters were detected as well as 225 infrastructure and support buildings. The number of shelters increased by approximately 17% since the previous UNOSAT analysis which used imagery from 06 December 2013 and located a total of 8,028 shelters. This is a preliminary analysis and has not yet been validated in the field; structure locations are subject to a spatial error margin of +/- three meters. Please send ground feedback to UNITAR-UNOSAT.

Tags: complex emergency-conflict-security, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected water extent and impact over Sylhet and Sunamganj Districts, Sylhet Division, Bangladesh as of 18 Jun

**UNOSAT code: FL20220525BGD**  This map illustrates satellite-detected surface waters and impact in Sylhet and Sunamganj Districts, Sylhet Division, Bangladesh as observed from a RCM-1 image acquired on 18 Jun 2022 at 17:54 local time. 
Within the analyzed area of about 1,325 km2, about 840 km2 of lands appear to be flooded. In this area, about 140 km of roads and 1 km of railway appear to be likely affected by the flood waters. Based on Worldpop population data and the detected surface waters, about 839,000 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 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)
Extensões de água superficias detectadas por satélite na Província de Nampula, Moçambique, no dia 13 de março de 2022

**UNOSAT code: TC20220311MOZ**  Este mapa ilustra água superficias detectadas por satélite nos distritos de Mossuril, Mogincual e Liúpo, Província de Nampula em Moçambique, no dia 13 de março, 2022, as 17:44 hora local. Dentro da área analisada de cerca de 14,000 km2, um total de 110 km2 aparece inundada. Baseado nos dados de população do Worldpop e na águas superficiais, cerca de 12,000 pessoas estão potencialmente expostos ou vivem perto das áreas inundadas.
Esto sao análises preliminar e ainda não foram verificados no terreno. Por favor, envie comentários United Nations Satellite Centre (UNOSAT).
Nota importante: Análises de inundaçãoes a partir de imagens radar podem subestimar a presença de águas acumuladas em áreas construídas e áreas de vegetação densa devido às propriedades de retrodifusão do sinal radar.

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected water extents in Midongy-Atsimo District, Atsimo Atsinanana Region, Madagascar as of 24 February 2022

**UNOSAT code: TC20220221MDG**  This map illustrates satellite-detected surface waters in Midongy-Atsimo District, Atsimo Atsinanana Region, Madagascar 
as observed from a Sentinel-2 image acquired on 24 February 2022 at 10:00 local time. Within the analyzed area about 1,200 km2, a total of about 90 km2 of lands appear to be flooded. 
Based on Worldpop population data and the detected surface waters, about 5,000 people are potentially exposed or living close to flooded areas. 
This is a preliminary analysis that has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected water extents in Vangaindrano district, Atsimo Atsinanana region, Madagascar as of 1 March 2022

**UNOSAT code: TC20220221MDG**  This map illustrates satellite-detected surface waters in Vangaindrano district, Atsimo Atsinanana region, Madagascar 
as observed from a Sentinel-2 image acquired on 1 March 2022 at 10:00 local time.  Within the analyzed cloud free zones of about 300km2, a total of about 25 km2 of lands appear to be flooded. 
Based on Worldpop population data and the detected surface waters, about 5,500 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 United Nations Satellite Centre (UNOSAT).

Tags: cyclones-hurricanes-typhoons, geodata
Organization: United Nations Satellite Centre (UNOSAT)
South Sudan - Damage density map, Nahr Yei district

This map illustrates satellite-detected damage density in and around the cities of Yei, Morobo and Koya, Nahr Yei district, South Sudan. Using satellite imagery acquired 05 March 2017, UNITAR - UNOSAT identified a total of 18,318 destroyed structures within an analysed area of about 3,640 square kilometers. Approximately 10'449 affected structures are found within Yei and along the four primary roads radiating from the city. About 7,870 destroyed structures are found along the road connecting the cities of Morobo and Koya. Most structures appear to be destroyed by fire. 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)