Datasets:

Pathange
/

tartanaviation-adsb-19k-clean

license: mit
task_categories:
  - tabular-regression
  - time-series-forecasting
tags:
  - aviation
  - adsb
  - aircraft-tracking
  - trajectories
  - reinforcement-learning
  - tabular-data
  - time-series
size_categories:
  - 10K<n<100K

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: aircraft_id, altitude_ft, ground_speed_kts, heading_deg
Location: latitude, longitude
Metadata: aircraft_tail, data_age_sec, range_nm, bearing_deg, altitude_is_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.read_csv('tartanaviation_adsb_19k_clean.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{tartanaviation_adsb_2024, 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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