Pathange/tartanaviation-adsb-19k-clean

TartanAviation ADS-B Dataset (19.7K Clean Samples)

Dataset Description

19,714 high-quality ADS-B trajectory datapoints from aircraft operations, rigorously cleaned and validated. Perfect for machine learning research in aviation, reinforcement learning, and trajectory prediction.

Key Features

• —19,714 clean samples (no missing data, no duplicates)
• —17 comprehensive features including aircraft ID, timestamp components, altitude, speed, heading, geolocation, and metadata
• —Data period: January to October 2022
• —Geographic coverage: Ohio airspace (KBTP airport region)
• —Formats available: CSV (human-readable) and JSONL (ML-optimized)

Dataset Structure (17 columns)

• —Temporal: year, month, day, hour, minute, second
• —Aircraft State: aircraftid, altitudeft, groundspeedkts, heading_deg
• —Location: latitude, longitude
• —Metadata: aircrafttail, dataagesec, rangenm, bearingdeg, altitudeis_gnss

Use Cases

• —Reinforcement learning for air traffic management
• —Aircraft trajectory prediction
• —Graph neural networks modeling
• —Aviation safety analysis
• —Time series forecasting for flight dynamics

Quick Start

import pandas as pd

Load CSV dataset df = pd.readcsv('tartanaviationadsb19kclean.csv') print(f"Dataset shape: {df.shape}") print(f"Unique aircraft: {df['aircraft_id'].nunique()}")

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Dataset Stats

• —Samples: 19,714
• —Features: 17
• —Unique Aircraft: 4,946
• —Time Period: January - October 2022
• —Region: Ohio airspace (KBTP airport area)
• —Data Quality: 100% complete, validated

Citation

@dataset{tartanaviationadsb2024, title={TartanAviation ADS-B Dataset (19.7K Clean Samples)}, author={Pathange}, year={2024}, publisher={Hugging Face}, url={https://huggingface.co/datasets/Pathange/tartanaviation-adsb-19k-clean} }

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