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    <identifier>10.57760/sciencedb.24849</identifier>
    <datestamp>2025-05-29T09:32:40Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2025-05-29</dc:date>
  <dc:title>A sample dataset for erosion gullies remote sensing recognition in typical Northeast China&amp;rsquo;s black soil region</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.24849</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>The agricultural activities are considerably affected by soil erosion, with erosion gullies representing a prominent manifestation of this issue in Northeast China&amp;rsquo;s black soil region. Therefore, accurate identification and detection of erosion gullies based on remote sensing are pivotal for safeguarding arable land. Here we provide a sample dataset for erosion gullies remote sensing recognition in typical Northeast China&amp;rsquo;s black soil region, i.e., Hailun City of Heilongjiang Province. The very high resolution remote sensing image is derived from Google Earth with a spatial resolution of 0.6 m/pixel. Each sample is 224*224 pixels in size with two categories, i.e., erosion gullies and else. A total of 10,000 samples are provided, with positive and negative samples in a ratio of 1:1. Besides, the sample dataset is split into training, validation and testing dataset with a ratio of 6:2:2. The sample dataset is from the paper in National Remote Sensing Bulletin, please refer to this paper for details.&amp;nbsp;</dc:description>
  <dc:subject>erosion gullies; black soil region in Northeast China; deep learning</dc:subject>
  <dc:creator>Feng Quanlong</dc:creator>
  <dc:creator>Jiang Zihang</dc:creator>
  <dc:creator>Niu Bowen</dc:creator>
  <dc:creator>Gao Bingbo</dc:creator>
  <dc:creator>Yang Jianyu</dc:creator>
  <dc:creator>Yang Ke</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/10.11834/jrs.20243139</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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