TartanAviation：面向终端空域运行的图像、语音与ADS-B轨迹数据集
TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations
AI总结：
本研究推出开源多模态数据集TartanAviation，同步采集终端空域的图像、空管语音与ADS-B轨迹数据，覆盖多类机场与多样运行场景，并配套开源采集预处理代码，为空管AI应用与自主飞机发展提供数据支撑。
摘要：
我们推出TartanAviation，这是一个聚焦终端空域运行的开源多模态数据集。TartanAviation通过安装在机场边界内的采集装置同步收集图像、语音以及ADS-B（广播式自动相关监视）轨迹数据，提供了机场环境的整体视图。该数据集在有塔台和无塔台的两类机场历经数月采集，以覆盖飞机运行、季节、机型和天气条件的多样性。总体而言，TartanAviation包含310万张图像、3374小时的空中交通管制语音数据，以及661天的ADS-B轨迹数据。所有数据均经过过滤、处理与验证，形成了经过整理的高质量数据集。除数据集外，我们还开源了用于数据采集和预处理的代码库，进一步提升了数据集的可访问性与易用性。我们认为该数据集具备诸多潜在应用场景，尤其对推动AI与机器学习技术融入空管系统、促进自主飞机在空域中的应用落地至关重要。
英文摘要：
We introduce TartanAviation, an open-source multi-modal dataset focused on terminal-area airspace operations. TartanAviation provides a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using setups installed inside airport boundaries. The datasets were collected at both towered and non-towered airfields across multiple months to capture diversity in aircraft operations, seasons, aircraft types, and weather conditions. In total, TartanAviation provides 3.1M images, 3374 hours of Air Traffic Control speech data, and 661 days of ADS-B trajectory data. The data was filtered, processed, and validated to create a curated dataset. In addition to the dataset, we also open-source the code-base used to collect and pre-process the dataset, further enhancing accessibility and usability. We believe this dataset has many potential use cases and would be particularly vital in allowing AI and machine learning technologies to be integrated into air traffic control systems and advance the adoption of autonomous aircraft in the airspace.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
