# Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports

arXiv: https://arxiv.org/abs/2501.07923 · HTML: https://mlsanity.com/paper/2501.07923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Sequential Classification of Aviation Safety Occurrences with Natural Language Processing](https://mlsanity.com/paper/2501.06490/) (2025)
- [Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences](https://mlsanity.com/paper/2501.06564/) (2025)

## Built on by (most cited first, 3 of 3)

- [Utilizing AI for Aviation Post-Accident Analysis Classification](https://mlsanity.com/paper/2506.00169/) (2025)
- [NOTAM-Evolve: A Knowledge-Guided Self-Evolving Optimization Framework with LLMs for NOTAM Interpretation](https://mlsanity.com/paper/2511.07982/) (2025)
- [Knots: A Large-Scale Multi-Agent Enhanced Expert-Annotated Dataset and LLM Prompt Optimization for NOTAM Semantic Parsing](https://mlsanity.com/paper/2511.12630/) (2025)

## Abstract (arXiv)

Aviation safety is paramount, demanding precise analysis of safety occurrences during different flight phases. This study employs Natural Language Processing (NLP) and Deep Learning models, including LSTM, CNN, Bidirectional LSTM (BLSTM), and simple Recurrent Neural Networks (sRNN), to classify flight phases in safety reports from the Australian Transport Safety Bureau (ATSB). The models exhibited high accuracy, precision, recall, and F1 scores, with LSTM achieving the highest performance of 87%, 88%, 87%, and 88%, respectively. This performance highlights their effectiveness in automating safety occurrence analysis. The integration of NLP and Deep Learning technologies promises transformative enhancements in aviation safety analysis, enabling targeted safety measures and streamlined report handling.