AndreaBozzo/ceres-open-data-index huggingface dataset:
Haiti: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Honduras: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Guatemala: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Grenada: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Ethiopia: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Colombia: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Democratic Republic of the Congo: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Cameroon: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Central African Republic: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Burkina Faso: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Burundi: Google Building Footprints

This large-scale open dataset consists of outlines of buildings derived from high-resolution 50 cm satellite imagery. It contains 1.8B building detections in Africa, Latin America, Caribbean, South Asia and Southeast Asia. The inference spanned an area of 58M km² and was carried out during May 2023.  
  

Each building in this dataset includes a polygon describing its footprint size on the ground and a confidence score indicating how sure this is a building. There is no information about the type of building, its street address, or any details other than its geometry.  
  

The HDX team curates these datasets at [Google Open Buildings (by HDX)](https://data.humdata.org/organization/google-open-buildings) using international boundaries from the UN Geospatial Hub.  
  

For more details, see the official website of the [Open Buildings dataset](https://sites.research.google/gr/open-buildings/).

Tags: facilities-infrastructure, geodata
Organization: Google Open Buildings (by HDX)
Peace and Security Pillar: Approved Peacekeeping Operations Budgets (Total Appropriations)

This dataset presents the total approved budget amounts for United Nations peacekeeping operations, as authorized by the General Assembly through resolutions on the financing of peacekeeping operations. For each mission and budget period, the dataset records the grand total appropriation approved, as stated in the relevant resolution text.  
  
The approved amounts reflect the full cost authorized for each mission, including all associated mission and shared support costs approved for the period, such as for the support account for peacekeeping operations, for the United Nations Logistics Base at Brindisi, Italy, and for the Regional Service Centre in Entebbe, Uganda.   
  
The dataset covers all peacekeeping operations active during the relevant time period, including UNTSO (United Nations Truce Supervision Organization) and UNMOGIP (United Nations Military Observer Group in India and Pakistan), which are funded through the regular budget rather than the peacekeeping budget.  
  
The data is extracted from Umoja Analytics.

Tags: complex emergency-conflict-security, peacekeeping
Organization: United Nations Peace and Security Data Hub
Satellite detected waters in Da Nang City of Viet Nam as of 30 October 2020

**UNOSAT code: TC20201028VNM**  This map illustrates satellite-detected surface waters in Da Nang City of Viet Nam as observed from a Sentinel-1 image acquired on 30 October 2020 at about 05:35 local time. Within the analyzed area of about 300 km2, a total of about 8 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 4,300 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)
Satellite detected waters in Nghe An Province of Viet Nam as of 9 November 2020

**UNOSAT code: TC20201028VNM**  This map illustrates satellite-detected surface waters in Nghe An Province of Viet Nam as observed from a Sentinel-1 image acquired on 9 November 2020 at 05:51 local time. Within the analyzed area of about 15,000 km2, a total of about 40 km2 of lands appear to be flooded. The water extent appears to have receded of about 360 km2 since 31 October 2020. Based on Worldpop population data and the detected surface waters, about 23,000 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)
Geodata of Satellite Detected Waters Over Can-Avid in Dolores Area, Eastern Samar, Eastern Visayas, Philippines

This map illustrates satellite-detected areas with waters as detected bySentinel-1 imagery acquired the 08 December 2014 in Can-Avid municipality, west of Dolores city, Eastern Samar Province (Philippines). The heavy rains related to the typhoon Hagupit induced areas with standing waters that affected 460 ha of agriculural fields. This analysis 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)
Zimbabwe: 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
South Africa: 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
Zambia: 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
United Republic of Tanzania: 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
Eswatini: 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
Satellite detected water extents between 05 and 09 October 2024 over South Sudan

**UNOSAT code: FL20240425SSD**  This map illustrates cumulative satellite-detected water using VIIRS in South Sudan between 05 to 09 October 2024 compared with the period from 13 to 17 September 2024 . Within the cloud free analysed areas of about 630,000 km², a total of about 44,000 km² of lands appear to be affected with flood waters. Water extent appears to have increased of about 2,800 km² since the period between 13 to 17 September 2024 . Based on Worldpop population data and the maximal flood water extent ~900,000 people are potentially exposed or living close to flooded areas.  
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected water extent in N'Djamena City as of 08 October 2024

**UNOSAT code: FL20240820TCD**  This map illustrates satellite-detected water extent in N'Djamena city, N'Djamena Department, Chad as observed from a WorldView-2 satellite image acquired on 08 October 2024. Within the analysed area of about 245 km², a total of about 34km² of lands appear to be affected by flood waters in Chad. Within the analysed area approximately 1,400 buildings (<5%) in Chad are affected by the floodwaters.  
According to the forecasted river flow data from ECMWF, as of 09 Oct. 2024, for the water gauges Chari A Chagoua and Chari A Ndjamena -Ex Fort-Lamy- along the river Chari, the current medium flow (Q2yr) has a tendency to decrease in the next 10 days with the peak likely happening on 10th Oct. 2024.   
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)
Satellite detected water extents from September to 07 October 2024 over Pakistan

**UNOSAT code: FL20240804PAK**  This map illustrates cumulative satellite-detected water using VIIRS in Pakistan from 24 September to 07 October 2024 compared with the period from 10 to 24 September 2024.   
Within the cloud free analysed areas of about 800,000 km², a total of about 29,000 km² of lands appear to be affected with flood waters. Water extent appears to have receded of about 4,500 km² since the period between 10 to 24 September 2024.   
Based on Worldpop population data and the maximum flood water extent ~8.6 million people are potentially exposed or living close to flooded areas.  
  
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

Tags: flooding, geodata
Organization: United Nations Satellite Centre (UNOSAT)