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        <datestamp>2026-09-18T04:15:14Z</datestamp>
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          <dc:title>A 10-m annual maize mapping dataset for Northeast China from 2017 to 2024</dc:title>
          <dc:creator>Xingguang Yan (23695269)</dc:creator>
          <dc:creator>Jingyi Le (25068425)</dc:creator>
          <dc:creator>Zhen Dong (1698988)</dc:creator>
          <dc:creator>Yuhuan Han (23222174)</dc:creator>
          <dc:creator>Lang Xia (5745266)</dc:creator>
          <dc:creator>Xinyi Guo (3755509)</dc:creator>
          <dc:subject>Agricultural land planning</dc:subject>
          <dc:subject>Earth and space science informatics</dc:subject>
          <dc:subject>Landscape ecology</dc:subject>
          <dc:subject>Cartography and digital mapping</dc:subject>
          <dc:subject>Land use and environmental planning</dc:subject>
          <dc:subject>Maize mapping</dc:subject>
          <dc:subject>Northeast China</dc:subject>
          <dc:subject>AlphaEarth Foundations</dc:subject>
          <dc:subject>Google Earth Engine</dc:subject>
          <dc:subject>Crop type classification</dc:subject>
          <dc:subject>Random forest</dc:subject>
          <dc:subject>Food security</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This dataset provides annual 10-m resolution maize distribution maps for Northeast China (Heilongjiang, Jilin, and Liaoning provinces) from 2017 to 2024. It is the first long-term, high-resolution maize-specific dataset for this region derived from AlphaEarth Foundations Satellite Embedding V1. The maps were produced on Google Earth Engine using random forest classifiers trained separately for five agro-climate zones across 119 grids. High-confidence training samples for 2017–2022 were extracted by harmonizing six existing crop products, while samples for 2023–2024 were migrated across years using cosine similarity (θ = 0.90) of the 64-dimensional embedding features. The dataset is stored in GeoTIFF format, with pixel values of 1 indicating maize and 0 indicating non-maize, covering the spatial extent of 38°42′–53°55′N and 118°53′–135°09′E. Independent validation against ground-truth samples (2017–2019) yielded maize F1 scores of 0.915–0.936, comparison with municipal statistical yearbook data (2017–2024) showed R² of 0.78–0.90, and test-sample validation produced overall accuracies of 0.934–0.985. The dataset supports research on food security, crop rotation, soil degradation, and climate adaptation in China's most important grain-producing region.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-18T04:15:14Z</dc:date>
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          <dc:identifier>10.6084/m9.figshare.33917041.v2</dc:identifier>
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