AutoLCZ：迈向基于规则遥感数据的局地气候分区自动化制图
AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing
AI总结：
提出AutoLCZ框架，利用LiDAR数据定义数值规则模拟LCZ定义，实现基于遥感数据的局地气候分区自动化制图，减少人工标注并提升可解释性，在纽约市验证了区分10种LCZ类型的能力。
摘要：
局地气候分区（LCZs）建立了一个标准分类系统，用于对景观空间进行分类，以改进城市气候研究。现有的LCZ制图依赖于人类与地理信息系统（GIS）的交互，或基于遥感（RS）数据进行建模。基于GIS的方法难以扩展到大面积区域。然而，基于RS的方法利用机器学习技术实现从RS数据自动进行LCZ分类。然而，基于RS的方法需要大量人工标注用于训练。我们提出了一种新颖的LCZ制图框架，称为AutoLCZ，用于从高分辨率RS模态中提取LCZ分类特征。我们研究了旨在模拟LCZ定义的数值规则的定义。这些规则从LiDAR数据中建模几何和地表覆盖属性。相应地，我们实现了在基于GIS的方案中从RS数据进行LCZ分类。所提出的AutoLCZ方法有潜力减少获取准确元数据所需的人力劳动。同时，AutoLCZ揭示了基于RS方法的物理可解释性。在纽约市（NYC）的概念验证中，我们利用机载LiDAR测量来建模4个LCZ特征，以区分10种LCZ类型。结果表明AutoLCZ作为从RS数据进行大规模LCZ制图的有前景途径的潜力。
英文摘要：
Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by human interaction with geographic information systems (GIS) or modelled from remote sensing (RS) data. GIS-based methods do not scale to large areas. However, RS-based methods leverage machine learning techniques to automatize LCZ classification from RS. Yet, RS-based methods require huge amounts of manual labels for training. We propose a novel LCZ mapping framework, termed AutoLCZ, to extract the LCZ classification features from high-resolution RS modalities. We study the definition of numerical rules designed to mimic the LCZ definitions. Those rules model geometric and surface cover properties from LiDAR data. Correspondingly, we enable LCZ classification from RS data in a GIS-based scheme. The proposed AutoLCZ method has potential to reduce the human labor to acquire accurate metadata. At the same time, AutoLCZ sheds light on the physical interpretability of RS-based methods. In a proof-of-concept for New York City (NYC) we leverage airborne LiDAR surveys to model 4 LCZ features to distinguish 10 LCZ types. The results indicate the potential of AutoLCZ as promising avenue for large-scale LCZ mapping from RS data.
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