# Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences

arXiv: https://arxiv.org/abs/2501.06564 · HTML: https://mlsanity.com/paper/2501.06564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Machine Learning for Air Transport Planning and Management](https://mlsanity.com/paper/2112.01301/) (2021)
- [Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language Processing](https://mlsanity.com/paper/2501.01227/) (2025)
- [Comparative Study of Deep Learning Architectures for Textual Damage Level Classification](https://mlsanity.com/paper/2501.01694/) (2025)

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

- [Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports](https://mlsanity.com/paper/2501.07923/) (2025)
- [Phase of Flight Classification in Aviation Safety using LSTM, GRU, and BiLSTM: A Case Study with ASN Dataset](https://mlsanity.com/paper/2501.07925/) (2025)
- [Utilizing AI for Aviation Post-Accident Analysis Classification](https://mlsanity.com/paper/2506.00169/) (2025)

## Abstract (arXiv)

The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identifying their causes and proposing safety recommendations. However, the narratives describing pre-accident events are presented in unstructured text that is not easily understood by computer systems. Classifying and categorizing safety occurrences based on these narratives can support informed decision-making by aviation industry stakeholders. In this study, researchers applied natural language processing (NLP) and artificial intelligence (AI) models to process text narratives to classify the flight phases of safety occurrences. The classification performance of two deep learning models, ResNet and sRNN was evaluated, using an initial dataset of 27,000 safety occurrence reports from the NTSB. The results demonstrated good performance, with both models achieving an accuracy exceeding 68%, well above the random guess rate of 14% for a seven-class classification problem. The models also exhibited high precision, recall, and F1 scores. The sRNN model greatly outperformed the simplified ResNet model architecture used in this study. These findings indicate that NLP and deep learning models can infer the flight phase from raw text narratives, enabling effective analysis of safety occurrences.