  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.
