Metric learning is the process of learning a tailored distance metric for a particular task. This advanced subfield of machine learning is useful to any machine learning or data mining task that relies on the computation of distances or similarities over objects. In recently years, machine learning techniques have been extensively used in aviation and aerospace engineering to make predictions, extract patterns, discover knowledge, etc. Nevertheless, metric learning, an element that can advance the performance of complex machine learning tasks, has so far been hardly utilized in relevant literature. In this study, we apply classic metric learning formulations with novel components on aviation environmental impact modeling. Through a weakly-supervised metric learning task, we achieve significant improvement in the newly emerged problem of aircraft characterization and segmentation for environmental impacts. The result will enable the more efficient and accurate modeling of aircraft environmental impacts, a focal topic in sustainable aviation. This work is also a demonstration that shows the potential and value of metric learning in a wide variety of similar studies in the transportation domain.
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