TAACKIT: Track Annotation and Analytics with Continuous Knowledge Integration Tool

TL;DR

TAACKIT is a novel tool designed to facilitate efficient annotation and validation of geospatial track data, significantly reducing annotation effort and enhancing ML model evaluation in domains like air traffic.

Contribution

It introduces a new tool for geospatial track annotation and validation, addressing a gap in ML data preparation for this domain.

Findings

Reduces annotation effort in geospatial data labeling

Improves ML model validation processes

Demonstrated effectiveness in air traffic domain

Abstract

Machine learning (ML) is a powerful tool for efficiently analyzing data, detecting patterns, and forecasting trends across various domains such as text, audio, and images. The availability of annotation tools to generate reliably annotated data is crucial for advances in ML applications. In the domain of geospatial tracks, the lack of such tools to annotate and validate data impedes rapid and accessible ML application development. This paper presents Track Annotation and Analytics with Continuous Knowledge Integration Tool (TAACKIT) to serve the critically important functions of annotating geospatial track data and validating ML models. We demonstrate an ML application use case in the air traffic domain to illustrate its data annotation and model evaluation power and quantify the annotation effort reduction.