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AviationGPT: A Large Language Model for the Aviation Domain
The advent of ChatGPT and GPT-4 has captivated the world with large language models (LLMs), demonstrating exceptional performance in question-answering, summarization, and content generation.
Adapting Sentence Transformers for the Aviation Domain
Learning effective sentence representations is crucial for many Natural Language Processing (NLP) tasks, including semantic search, semantic textual similarity (STS), and clustering.
Remote Sensing Scene Classification with Masked Image Modeling (MIM)
As Masked Image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classification task.
Aerial Image Object Detection With Vision Transformer Detector (ViTDet)
Our results show that ViTDet can consistently outperform its convolutional neural network counterparts on horizontal bounding box (HBB) object detection by a large margin (up to 17% on average precision) and that it achieves the competitive performance for oriented bounding box (OBB) object detection.
Flight Demand Forecasting with Transformers
This work was conducted in support of a MITRE-developed mobile application, Pacer, which displays predicted departure demand to general aviation (GA) flight operators so they can have better situation awareness of the potential for departure delays during busy periods.
Multi-Airport Delay Prediction with Transformers
Traffic, demand, weather, and traffic management actions are all critical inputs to any prediction model.
Helicopter Track Identification with Autoencoder
Our approach can also identify mislabeled aircraft types in the flight track data and find true types for records with pseudo aircraft type labels such as HELO.
Deep Learning for Flight Demand Forecasting
Field demonstrations involving Pacer's previously designed rule-based prediction method showed that the prediction accuracy of departure demand still has room for improvement.
Autoencoding Features for Aviation Machine Learning Problems
The current practice of manually processing features for high-dimensional and heterogeneous aviation data is labor-intensive, does not scale well to new problems, and is prone to information loss, affecting the effectiveness and maintainability of machine learning (ML) procedures.
