论文标题
开发高质量的培训样本，用于韩国的基于深度学习的本地气候区分类
Developing High Quality Training Samples for Deep Learning Based Local Climate Zone Classification in Korea
论文作者
论文摘要
正如联合国预测的那样，到2050年，三分之二的人将居住在城市地区，强调需要可持续的城市发展和监测。常见的城市足迹数据提供了高分辨率的城市范围，但缺乏有关分布，模式和特征的基本信息。当地气候区（LCZ）提供了一个高效且标准化的框架，可以描述城市地区的内部结构和特征。已经探索了全球尺度的LCZ映射，但受到较低精度，可变标签质量或域适应性挑战的限制。取而代之的是，这项研究开发了一种自定义LCZ数据，以使用多尺度卷积神经网络绘制韩国关键城市。结果表明，与传统的基于社区的LCZ映射以及使用机器学习以及全局SO2SAT数据集的转移学习相比，使用带有深入学习的新颖的自定义LCZ数据可以生成更准确的LCZ地图结果。
Two out of three people will be living in urban areas by 2050, as projected by the United Nations, emphasizing the need for sustainable urban development and monitoring. Common urban footprint data provide high-resolution city extents but lack essential information on the distribution, pattern, and characteristics. The Local Climate Zone (LCZ) offers an efficient and standardized framework that can delineate the internal structure and characteristics of urban areas. Global-scale LCZ mapping has been explored, but are limited by low accuracy, variable labeling quality, or domain adaptation challenges. Instead, this study developed a custom LCZ data to map key Korean cities using a multi-scale convolutional neural network. Results demonstrated that using a novel, custom LCZ data with deep learning can generate more accurate LCZ map results compared to conventional community-based LCZ mapping with machine learning as well as transfer learning of the global So2Sat dataset.
