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    <identifier>10.11922/sciencedb.j00001.00321</identifier>
    <datestamp>2022-06-27T11:01:59Z</datestamp>
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  <dc:date>2022-06-27</dc:date>
  <dc:title>A dataset of boundary and length of glaciers in Qinghai  province in 2020</dc:title>
  <dc:identifier>doi:10.11922/sciencedb.j00001.00321</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Qinghai province (31&amp;deg;39&amp;prime;~39&amp;deg;19&amp;prime;N, 89&amp;deg;35&amp;prime;~103&amp;deg;04&amp;prime;E) in the northeastern Qinghai-Tibet Plateau possesses numerous modern glaciers, which are widely developed in high altitude mountains such as Kunlun Mountains, Tanggula Mountains, Qilian Mountains, Altun&amp;nbsp;Mountains and A'Ny&amp;ecirc;maq&amp;ecirc;n&amp;nbsp;Mountains. Under global warming, the glaciers had&amp;nbsp;been retreating in Qinghai province in recent years. The knowledge of the contemporary glaciers is the basis for scientific evaluation of the response pattern&amp;nbsp;of glacier changes on climate change and the formulation of policies for rational use of water resources. Based on the GF-1/2/6&amp;nbsp;high resolution remote sensing images from&amp;nbsp;2018 to 2021&amp;nbsp;and SRTM DEM data, a&amp;nbsp;vector&amp;nbsp;dataset of boundary and length of glaciers in Qinghai province were produced using Deep Learning method and automatic extraction method of glacier centerline,&amp;nbsp;the accuracy is 99.01% and 98.09%,&amp;nbsp;respectively. The dataset can&amp;nbsp;reflect the status of glaciers in Qinghai province in 2020&amp;nbsp;and&amp;nbsp;provide data to support the research of glacier and climate change.</dc:description>
  <dc:subject>Glacier; GF image; Deep Learning; Qinghai province</dc:subject>
  <dc:creator>XUE Jiao</dc:creator>
  <dc:creator>YAO Xiaojun</dc:creator>
  <dc:creator>CHU Xinde</dc:creator>
  <dc:creator>Pang Wenlong</dc:creator>
  <dc:creator>ZHANG Cong</dc:creator>
  <dc:creator>ZHOU Sugang</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.11922/11-6035.csd.2021.0082.zh</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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