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  • 200+ Downloads
    Updated August 16, 2018 | Dataset date: Sep 13, 2012
    This dataset updates: Every year
    Sri Lankan main road network Scale : 250k ArcGIS Projected Coordinate System : SLD-Kandawala.prj
  • 300+ Downloads
    Updated August 16, 2018 | Dataset date: Apr 16, 2013
    This dataset updates: Every year
    Main river and sream of Sri Lanka Scale : 250k ArcGIS Projected Coordinate System : SLD-Kandawala.prj
  • 20+ Downloads
    Updated October 20, 2017 | Dataset date: Jan 1, 2008-Dec 31, 2027
    This dataset updates: Live
    List of airports in Sri Lanka, with latitude and longitude. Unverified community data from http://ourairports.com/countries/LK/
  • 30+ Downloads
    Updated September 15, 2017 | Dataset date: Jan 7, 2015
    This dataset updates: Never
    This map illustrates satellite-detected areas of flood water as observed in Sentinel-1 imagery collected 24 November 2014 and 18 December 2014. Waters extended along coastal areas and shores of inland lakes, with few large bodies of flood waters detected. Numerous roads and railroads are likely inundated by flood waters which may impede transport in those areas. It is likely that flood waters have been systematically underestimated in highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.
  • 500+ Downloads
    Updated June 8, 2017 | Dataset date: Jun 1, 2017
    This dataset updates: Never
    Product This priority index was derived by combining a detailed flood extent mapping with detailed human settlement geo-data. Both sources were combined to produce the location and magnitude of population living in flooded areas. This was subsequently aggregated to admin-4 areas (GND) as well as admin-3 areas (DS divisional). The flood extent mapping was derived in turn by combining two sources: Flood extent maps could be produced rather faster using satellite imageries captured by either optical sensors or Synthetic Aperture Radar (SAR) sensors. In most places flood is cause by heavy rainfall which means in most cases cloud is present, this is a limitation for optical sensors as they can’t penetrate clouds. Radar sensors are not affected by cloud, which make them more useful in presence of cloud. In This analysis we analyzed sentinel2 optical image from May 28th and Sentinel 1 SAR image from May 30th. Then we combine the two results adding up the flood extents. Main cloud covered areas and permanent water bodies are removed from the flood extent map using the Sentinel 2 cloud mask. The scale/resolution of the flood extent map is 30mts where as the permanent water body map has 250m scale resolution. This will introduce some discrepancy: part of flood extent map could be permanent water body. Scope Analysis focused on 4 districts in South-West Sri Lanka based on news reports (https://www.dropbox.com/s/n0qdqe7qfgq6fyv/special_situation.pdf?dl=0). Based on the admin-3-level analysis, highest percentages of population living in flooded areas were seen in Matara district. Admin-4 level analysis concentrated only on Matara district for that reason. Caveats The dataset is showing percentage flooded. The data has not yet been corrected for small populations. We believe the product is currently pointing to the high priority areas. In the shp or csv files the user of this data could easily correct for small populations, if there is a wish to target on the amount of people affected. Data used from partners The human settlement data was retrieved from http://ciesin.columbia.edu/data/hrsl/. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-06-2017. The Radar imagery analysis was done by NASA JPL, whose input in this product has been crucial. Visualization An example map is available here: http://bit.ly/SriLankaFloodMap Linked data Admin boundaries 3 and 4 can be found here (link on OBJECT_ID): https://data.humdata.org/group/lka?q=&ext_page_size=25&sort=score+desc%2C+metadata_modified+desc&tags=administrative+boundaries#dataset-filter-start How to use The ratio column in the SHPs or CSVs can be multiplied by 100 to get the percentage of flooding in the area.
  • 600+ Downloads
    Updated October 4, 2016 | Dataset date: Oct 1, 2015
    This dataset updates: Every year
    Disaster loss and damage dataset for Sri Lanka
  • 400+ Downloads
    Updated July 31, 2016 | Dataset date: Dec 31, 2012
    This dataset updates: Every year
    This data set include Sri Lanka census of population and housing 2012 with sex age and religion dis-aggregated data up to GND (4th Admin) level . This data set is shared by Disaster Management Centre of Sri Lanka during the 2016 flood response only for humanitarian response/agency purposes and updated with population projection up to 2022 by WFP and UNOCHA.
  • 90+ Downloads
    Updated January 29, 2016 | Dataset date: Dec 31, 2015
    This dataset updates: Never
    The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
  • 20+ Downloads
    Updated August 10, 2015 | Dataset date: Dec 31, 2014
    This dataset updates: Never
    This map illustrates satellite-detected areas of probable flood waters as detected in a Radarsat-2 satellite image collected 30 December 2014 and Sentinel-1 data collected 18 December 2014. Detected flood waters are primarily concentrated along coastal areas and shores of inland lakes, with few large bodies of flood waters detected. Numerous roads and railroads are likely inundated by flood waters which may impede transport in those areas. It is likely that flood waters have been systematically underestimated in highly vegetated areas along main river banks, and within built-up urban areas because of the characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.