  Accurate modeling of aircraft environmental impact is pivotal to the design
of operational procedures and policies to mitigate negative aviation
environmental impact. Aircraft environmental impact segmentation is a process
which clusters aircraft types that have similar environmental impact
characteristics based on a set of aircraft features. This practice helps model
a large population of aircraft types with insufficient aircraft noise and
performance models and contributes to better understanding of aviation
environmental impact. Through measuring the similarity between aircraft types,
distance metric is the kernel of aircraft segmentation. Traditional ways of
aircraft segmentation use plain distance metrics and assign equal weight to all
features in an unsupervised clustering process. In this work, we utilize
weakly-supervised metric learning and partial information on aircraft fuel
burn, emissions, and noise to learn weighted distance metrics for aircraft
environmental impact segmentation. We show in a comprehensive case study that
the tailored distance metrics can indeed make aircraft segmentation better
reflect the actual environmental impact of aircraft. The metric learning
approach can help refine a number of similar data-driven analytical studies in
aviation.
