Improved Aircraft Environmental Impact Segmentation via Metric Learning

TL;DR

This paper introduces a weakly-supervised metric learning approach to improve aircraft environmental impact segmentation, enabling more accurate clustering based on aircraft features and impact characteristics.

Contribution

It presents a novel application of metric learning to tailor distance metrics for aircraft impact segmentation, enhancing the reflection of actual environmental impacts.

Findings

Tailored metrics improve aircraft segmentation accuracy.

Enhanced clustering better reflects environmental impact.

Method benefits data-driven aviation impact studies.

Abstract

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.