Urban land use structures impact local climate conditions of metropolitan
areas. To shed light on the mechanism of local climate wrt. urban land use, we
present a novel, data-driven deep learning architecture and pipeline,
DeepLCZChange, to correlate airborne LiDAR data statistics with the Landsat 8
satellite's surface temperature product. A proof-of-concept numerical
experiment utilizes corresponding remote sensing data for the city of New York
to verify the cooling effect of urban forests.

本研究利用数据驱动的深度学习体系结构和方法探究了城市土地利用结构对城市气候的影响，并通过纽约市的相关遥感数据验证了城市森林的冷却效应。

DeepLCZChange：一种用于城市气候韧性的遥感深度学习模型架构

DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for  Urban Climate Resilience

We present and evaluate a weakly-supervised methodology to quantify the
spatio-temporal distribution of urban forests based on remotely sensed data
with close-to-zero human interaction. Successfully training machine learning
models for semantic segmentation typically depends on the availability of
high-quality labels. We evaluate the benefit of high-resolution,
three-dimensional point cloud data (LiDAR) as source of noisy labels in order
to train models for the localization of trees in orthophotos. As proof of
concept we sense Hurricane Sandy's impact on urban forests in Coney Island, New
York City (NYC) and reference it to less impacted urban space in Brooklyn, NYC.

本文研究采用弱监督方法，利用远程感知数据对城市森林进行时空分布量化，并探索利用高分辨率 LiDAR 数据为训练提供嘈杂标签的方法，以探测飓风桑迪带来的影响，实现树木的地理定位。通过针对纽约市的考察，验证研究结果的可靠性。