基于深度学习网络的36个中国城市的细粒度城市蓝绿灰色景观数据集
1College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.
Scientific data
|March 4, 2024
概括
研究人员为36个中国城市创建了一个高分辨率的城市蓝绿灰 (UBGG) 景观数据集. 这种详细的绘图提高了对复杂的城市生态系统和人与自然相互作用的理解.
科学领域:
- 环境科学 环境科学
- 城市生态城市生态学
- 遥感 遥感 遥感 遥感
背景情况:
- 精确地绘制城市景观,特别是城市蓝绿灰 (UBGG) 连续体,对于理解城市系统中人与自然的相互作用至关重要.
- 城市环境的空间异质性给大规模,细粒度的绘图工作带来了重大挑战.
研究的目的:
- 为了生成36个中国大都市的高分辨率 (3米) UBGG景观数据集.
- 开发一种可转移的深度学习模型,用于准确和通用的城市景观分类.
- 为分析城市生态系统动态提供全面的数据集.
主要方法:
- 利用336个行星卫星图像创建了一个3米分辨率的UBGG景观数据集 (UBGG-3m).
- 开发并应用了可转移的多尺度高分辨率卷积神经网络.
- 为模型培训和概括,编制了一个大量的UBGG景观样本数据集 (UBGGset).
主要成果:
- 在UBGG景观的分类中取得了卓越的准确性,总准确率为91.2%,在验证中FWIoU为83.9%.
- 证明了数据集能够捕捉不同地理区域复杂的城市景观异质性的能力.
- 该数据集为36个中国大都市提供了前所未有的细节.
结论:
- 开发的UBGG-3m数据集为中国大都市的城市景观绘制提供了显著的进步.
- 可转移的深度学习方法证明了对大规模,高分辨率的城市景观分类的有效性.
- 这一数据集为复杂的城市环境提供了有价值的见解,支持城市规划和生态研究.
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