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<p>This study releases China-MAS-50k, i.e., China Multi-label dataset for Agriculture &amp; rural Scene 50k, the first very-high-resolution (VHR) remote sensing dataset for multi-label classification covering entire China’s agricultural and rural areas, filling the gap in finely annotated data for non-urban scene recognition. Based on a 50 km grid system, over 50,000 sample points were determined nationwide, where VHR Google Earth imagery were to be collected for subsequent multi-label annotation. A fine-grained label system comprising 18 categories (e.g., cropland, rural village, greenhouse and photovoltaic station, etc.) was established. Meanwhile, both a rigorously defined visual interpretation system and a labeling procedure including cross-check and error correction were proposed to maintain annotation quality. Finally, the proposed dataset has a total of 55,520 VHR images with 135,289 labels, which exhibits a long-tail distribution thus providing a challenging benchmark dataset. Furthermore, we evaluated the performance of mainstream multi-label classification models on the China-MAS-50k dataset, where ResNeXt-101 achieved the best performance with an F1-score of 78.4%, but exhibited limitations in recognizing tail categories.</p>

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A multi-label dataset for China’s agricultural and rural scenes classification from VHR satellite imagery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月7日

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  • Shiying Yuan,
  • Quanlong Feng,
  • Bowen Niu,
  • Xiaolu Yan,
  • Landi Zheng,
  • Zinuo Hao,
  • Dehai Zhu,
  • Jianyu Yang,
  • Jiantao Liu

摘要

This study releases China-MAS-50k, i.e., China Multi-label dataset for Agriculture & rural Scene 50k, the first very-high-resolution (VHR) remote sensing dataset for multi-label classification covering entire China’s agricultural and rural areas, filling the gap in finely annotated data for non-urban scene recognition. Based on a 50 km grid system, over 50,000 sample points were determined nationwide, where VHR Google Earth imagery were to be collected for subsequent multi-label annotation. A fine-grained label system comprising 18 categories (e.g., cropland, rural village, greenhouse and photovoltaic station, etc.) was established. Meanwhile, both a rigorously defined visual interpretation system and a labeling procedure including cross-check and error correction were proposed to maintain annotation quality. Finally, the proposed dataset has a total of 55,520 VHR images with 135,289 labels, which exhibits a long-tail distribution thus providing a challenging benchmark dataset. Furthermore, we evaluated the performance of mainstream multi-label classification models on the China-MAS-50k dataset, where ResNeXt-101 achieved the best performance with an F1-score of 78.4%, but exhibited limitations in recognizing tail categories.