  Air traffic trajectory recognition has gained significant interest within the
air traffic management community, particularly for fundamental tasks such as
classification and clustering. This paper introduces Aircraft Trajectory
Segmentation-based Contrastive Coding (ATSCC), a novel self-supervised time
series representation learning framework designed to capture semantic
information in air traffic trajectory data. The framework leverages the
segmentable characteristic of trajectories and ensures consistency within the
self-assigned segments. Intensive experiments were conducted on datasets from
three different airports, totaling four datasets, comparing the learned
representation's performance of downstream classification and clustering with
other state-of-the-art representation learning techniques. The results show
that ATSCC outperforms these methods by aligning with the labels defined by
aeronautical procedures. ATSCC is adaptable to various airport configurations
and scalable to incomplete trajectories. This research has expanded upon
existing capabilities, achieving these improvements independently without
predefined inputs such as airport configurations, maneuvering procedures, or
labeled data.
