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。

从自动生成分割地图中监测城市森林

Monitoring Urban Forests from Auto-Generated Segmentation Maps

This article proposes a method to quantitatively measure the resilience of
transportation systems using GPS data from taxis. The granularity of the GPS
data necessary for this analysis is relatively coarse; it only requires
coordinates for the beginning and end of trips, the metered distance, and the
total travel time. The method works by computing the historical distribution of
pace (normalized travel times) between various regions of a city and measuring
the pace deviations during an unusual event. This method is applied to a
dataset of nearly 700 million taxi trips in New York City, which is used to
analyze the transportation infrastructure resilience to Hurricane Sandy. The
analysis indicates that Hurricane Sandy impacted traffic conditions for more
than five days, and caused a peak delay of two minutes per mile. Practically,
it identifies that the evacuation caused only minor disruptions, but
significant delays were encountered during the post-disaster reentry process.
Since the implementation of this method is very efficient, it could potentially
be used as an online monitoring tool, representing a first step toward
quantifying city scale resilience with coarse GPS data.

本文介绍了一种使用出租车 GPS 数据定量测量交通系统弹性的方法，通过计算城市各个区域之间的历史速度分布并测量异常事件期间的速度偏差，该方法应用于纽约市近 7 亿辆出租车的数据集，可用于分析与飓风桑迪相关的交通基础设施弹性，该分析表明飓风桑迪影响交通状况超过 5 天，每英里最高时延为 2 分钟，并可潜在地用作在线监测工具。