Data Datasets [198] | Archived Datasets[0] [?] Show filter:
Refine your search: Clear all
Featured:
Locations:
More
Formats:
More
Organisations:
More
Tags:
More
Licenses:
More
  • 800+ Downloads
    Updated 9 October 2020 | Dataset date: August 11, 2020-August 11, 2020
    This dataset updates: Every week
    Beirut port explosion operational zones UPDATED 9 October 2020: Enlarged zones with UN HABITAT socio-economic classification and the 3Km priority of the Flash Appeal priority radius added. Please see the methodology section in the metadata for further information. UPDATED 19 August 2020: 11 operational zones numbered 129 to 139 inclusive added to the Sinn El-Fil cadastre south of the origional set. Zones spreadsheet added. NOTES Users are referred to the compatible Beirut buildings footprints COD. The standard Administrative Boundary Common Operational Dataset (COD-AB) can be found at Lebanon - Subnational Administrative Boundaries.
  • 80+ Downloads
    Updated 11 August 2020 | Dataset date: July 24, 2020-July 24, 2020
    This dataset updates: Never
    UNOSAT code: FL20200713BGD This map illustrates potentially exposed population to floods (cumulative) aggregated by district using NOAA20-VIIRS in Bangladesh between the 12th and the 21st of July 2020 and Worldpop spatial demographic data. About 34 million people were exposed or living close to flooded areas. The most exposed districts mainly located in Sylhet, Mymensingh and Rajshahi divisions.
  • 10+ Downloads
    Updated 24 July 2020 | Dataset date: July 22, 2020-July 22, 2020
    This dataset updates: Never
    UNOSAT code: FL20200713BGD This map illustrates satellite-detected surface waters over Khulna, Rajshahi and Rangpur Division of Bangladesh of Bangladesh as observed from a Sentinel-1 image acquired on 9 July 2020 and 21 July 2020. Within the analyzed area of about 15,000 km2, a total of about 3,600 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 2,507,000 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 Sentinel-1 image acquired on 9 July 2020 and 21 July 2020 may underestimate the presence of standing
  • 10+ Downloads
    Updated 24 July 2020 | Dataset date: July 22, 2020-July 22, 2020
    This dataset updates: Never
    UNOSAT code: FL20200713BGD This map illustrates satellite-detected surface waters over Chittagong, Dhaka, Mymensingh and Sylhet Divisions of Bangladesh as observed from a Sentinel-1 image acquired on 20 July 2020 due to the current monsoon rains. Within the analyzed area of about 14,250 km2, a total of about 7,120 km2 of lands appear to be flooded. Based on WorldPop population data and the detected surface waters, about 6,700,000 people area 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.
  • 10+ Downloads
    Updated 24 July 2020 | Dataset date: July 21, 2020-July 21, 2020
    This dataset updates: Never
    UNOSAT code: FL20200713BGD This map illustrates satellite-detected surface waters in the central parts of Bangladesh as observed from a Sentinel-1 image acquired on 19 July 2020 and 13 July 2020. Within the analyzed area of about 40,000 km2, a total of about 8,700 km2 of lands appear to be flooded. 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 Sentinel-1 imagery acquired on 19 July 2020 and 13 July 2020 may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • Updated 24 July 2020 | Dataset date: July 21, 2020-July 21, 2020
    This dataset updates: Never
    UNOSAT code: FL20200713BGD This map illustrates satellite-detected surface waters over the Eastern part of the Sylhet division of Bangladesh as observed from a TerraSAR-X image acquired on 18 July 2020 due to the current monsoon rains. Within the analyzed area of about 8,600 km2, a total of about 2,200 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Based on WorldPop population data and the detected surface waters, about 2,000,000 people area potentially exposed or living close to flooded areas. 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.
  • 10+ Downloads
    Updated 24 July 2020 | Dataset date: July 16, 2020-July 16, 2020
    This dataset updates: Never
    UNOSAT code: FL20200708NPL This map illustrates satellite-detected surface waters in Province 2 of Nepal as observed from a Radarsat-2 image acquired on 14 July 2020 due to the current monsoon rains. Within the analyzed area of about 2,000 km2, a total of about 50 km2 of lands appear to be flooded. 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.
  • Updated 24 July 2020 | Dataset date: July 15, 2020-July 15, 2020
    This dataset updates: Never
    UNOSAT code: FL20200708NPL This map illustrates satellite-detected surface waters in Province 2 of Nepal as observed from a Sentinel-1 image acquired on 13 July 2020 due to the current monsoon rains. Within the analyzed area of about 2,300 km2, a total of about 200 km2 of lands appear to be flooded. 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.
  • Updated 24 July 2020 | Dataset date: July 13, 2020-July 13, 2020
    This dataset updates: Never
    UNOSAT code: FL20200708NPL This map illustrates satellite-detected surface waters in Province 1 and 2 of Nepal as observed from a Radarsat-2 image acquired on 12 July 2020 due to the current monsoon rains. Within the analyzed area of about 3,000 km2, a total of about 475 km2 of lands appear to be flooded. 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 due to backscattering properties of the radar signal.
  • Updated 24 July 2020 | Dataset date: July 14, 2020-July 14, 2020
    This dataset updates: Never
    UNOSAT code: FL20200708NPL This map illustrates satellite-detected surface waters in Province 2 of Nepal as observed from a TerraSAR-X image acquired on 12 July 2020 due to the current monsoon rains. Within the analyzed area of about 900 km2, a total of about 35 km2 of lands appear to be flooded. 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.
  • Updated 24 July 2020 | Dataset date: July 09, 2020-July 09, 2020
    This dataset updates: Never
    UNOSAT code: FL20200708NPL This map illustrates satellite-detected surface waters in Province 1 and 2 of Nepal as observed from a Sentinel-1 image acquired on 6 July 2020. Within the analyzed area of about 2,700 km2, a total of a bout 76 km2 of lands appear to be flooded. 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 Sentinel-1 imagery acquired on 6 July 2020 may seriously underestimate presence of standing flood water in built up areas due to backscattering properties
  • Updated 24 July 2020 | Dataset date: July 20, 2020-July 20, 2020
    This dataset updates: Never
    UNOSAT code: FL20200713BGD This map illustrates satellite-detected surface waters over Mymensingh, Rajshahi and Rangpur Division of Bangladesh as observed from a TerraSAR-X image acquired on 18 July 2020. Within the analyzed area of about 9,000 km2, a total of about 2,860 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please sendground 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.
  • Updated 24 July 2020 | Dataset date: July 20, 2020-July 20, 2020
    This dataset updates: Never
    UNOSAT code: FL20200713BGD This map illustrates satellite-detected surface waters over Bangladesh as observed from a TanDEM-X image acquired on 19 July 2020 due to the current monsoon rains. Within the analyzed area of about 14,000 km2, a total of about 6,800 km2 of lands appear to be flooded. 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.
  • Updated 24 July 2020 | Dataset date: July 08, 2020-July 08, 2020
    This dataset updates: Never
    UNOSAT code: FL20200626UKR This map illustrates satellite-detected surface waters in Ivano-Frankivska and Ternopilska Oblastof Ukraine as observed from a Sentinel-1 image acquired on 24 June 2020. Within the ana lyzed area of about 642 km2, a total of a bout 35 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT .
  • 10+ Downloads
    Updated 3 June 2020 | Dataset date: June 02, 2020-June 02, 2020
    This dataset updates: Never
    UNOSAT code: TC20200518BGD This map illustrates satellite-detected surface waters in Bagerhat, Jessore, Khulna, Narail, and Satkhira district of Khulna division, Bangladesh as observed from a Sentinel-1 image acquired on 31 May 2020. Within the analyzed area of about 8,320 km2, a total of about 462 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 30+ Downloads
    Updated 3 June 2020 | Dataset date: May 26, 2020-May 26, 2020
    This dataset updates: Never
    UNOSAT code: TC20200518BGD This map illustrates satellite-detected surface waters in Bagerhat, Jessore, Khulna, Narail, and Satkhira district of Khulna division, Bangladesh as observed from a Sentinel-1 image acquired on 25 May 2020. Within the analyzed area of about 8,320 km2, a total of about 1,000 km2 of lands appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 28, 2020-April 28, 2020
    This dataset updates: Never
    UNOSAT code: TC20200403VUT This map illustrates potentially damaged structures and buildings in Meawo Island, Penama Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis used a Pleiades image acquired on 21 and 27 April 2020 as post event images, Within the island boundary, UNITAR-UNOSAT identified in the cloud free zones about 20 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 2% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 29, 2020-April 29, 2020
    This dataset updates: Never
    UNOSAT code: TC20200403VUT This map illustrates potentially damaged structures and buildings in West Santo and South Santo Municipality, Sanma Province, Vanuatu as detected using WorldView-2 satellite images acquired on 17 and 19 April 2020 and a Pleiades satellite image acquired on 27 April 2020. Within the analyzed zone, UNITAR-UNOSAT identified in the cloud-free zones about 3,650 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 63 % of the total number of structures within the analyzed cloud-free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 27, 2020-April 27, 2020
    This dataset updates: Never
    UNOSAT code: TC20200403VUT This map illustrates potentially damaged structures and buildings in Ambae Island, Penama Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis used a WorldView-2 image acquired on 20 April 2020 and Pleiades image acquired on 21 April 2020 as post event images, Within the Island extent, UNITAR-UNOSAT identified in the cloud free zones about 60 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 1% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 24, 2020-April 24, 2020
    This dataset updates: Never
    UNOSAT code: TC20200403VUT This map illustrates potentially damaged structures and buildings in Paama Island, Malampa Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis used a Pleiades image acquired on 21 April 2020 as post event images, Within the island boundary, UNITAR-UNOSAT identified in the cloud free zones about 100 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 30% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 17, 2020-April 17, 2020
    This dataset updates: Never
    UNOSAT code: TC20200408FJI This map illustrates potentially damaged structures, buildings and their related density in Kadavu Province, Eastern Division, Republic of Fiji as detected by satellite images acquired on 12 April 2020. Within the Kadavu Province, UNITAR-UNOSAT identified in the cloud free zones 1,003 potentially damaged structures. Taking into account the pre-building footprints provided by OpenStreetMap, this represents about 22% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 16, 2020-April 16, 2020
    This dataset updates: Never
    UNOSAT code: TC20200403VUT This map illustrates potentially damaged structures and buildings in south of Sanma Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis identified about 5,200 potentially damaged structures in the analysis extent across the south of Sanma province within the cloud free areas. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 33% of the total number of structures/buildings within the analyzed cloud free areas. Please note that some areas could not be analyzed due to the cloud cover. Total and final estimates by municipality are summarized in the table below. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 15, 2020-April 15, 2020
    This dataset updates: Never
    UNOSAT code: TC20200408FJI This map illustrates potentially damaged structures and buildings in Ono-i-Lau, Lau Province, Eastern Division, Republic of Fiji as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 08 April 2020. UNITAR-UNOSAT analysis used a Pleiades image acquired on 10 April 2020. Within Ono District, UNITAR-UNOSAT identified 23 potentially damages structures. Taking into account the pre-building footprints provided by OpenStreetMap, this represents about 8% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 14, 2020-April 14, 2020
    This dataset updates: Never
    UNOSAT code: TC20200408FJI This map illustrates potentially damaged structures and buildings in Kadavu Province, Eastern Division, Republic of Fiji as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 08 April 2020. UNITAR-UNOSAT analysis used a Pleiades image acquired on 12 April 2020. Within the Kadavu Province boundary, UNITAR-UNOSAT identified in the cloud free zones 1,003 potentially damaged structures. Taking into account the pre-building footprints provided by OpenStreetMap, this represents about 22% of the total number of structures within the analyzed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • Updated 3 June 2020 | Dataset date: April 10, 2020-April 10, 2020
    This dataset updates: Never
    UNOSAT code: TC20200403VUT This map illustrates potentially damaged structures and buildings in Malampa Province, Vanuatu as detected by satellite image acquired after landfall of the Tropical Cyclone Harold-20 on 6 April 2020. UNITAR-UNOSAT analysis used a Pleiades image acquired on 7, 8 and 9 April 2020 as post event images. Within the analysis extent, UNITAR-UNOSAT identified in the cloud free zones 25 potentially damaged structures. Taking into account the pre-building footprints provided by OpenStreetMap, this represents less than 1 % of the total number of structures within the analyzed cloud-free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.