TAACKIT: Track Annotation and Analytics with Continuous Knowledge Integration Tool
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910758648938496 |
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| author | Lee, Lily Fontes, Julian Weinert, Andrew Schomacker, Laura Stabile, Daniel Hou, Jonathan |
| author_facet | Lee, Lily Fontes, Julian Weinert, Andrew Schomacker, Laura Stabile, Daniel Hou, Jonathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16228 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TAACKIT: Track Annotation and Analytics with Continuous Knowledge Integration Tool Lee, Lily Fontes, Julian Weinert, Andrew Schomacker, Laura Stabile, Daniel Hou, Jonathan Artificial Intelligence Machine Learning 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. |
| title | TAACKIT: Track Annotation and Analytics with Continuous Knowledge Integration Tool |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.16228 |