  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.
