As data of aircraft movements have become freely accessible on a large scale through means of crowdsourcing, their open source intelligence (OSINT) value has been illustrated in many different domains. Potentially sensitive movements of all stakeholders outside commercial aviation are potentially affected, from corporate jets to military and government aircraft. Until now, this OSINT value was shown only on historical data, where automated analysis on flight destinations has been effective to find information on potential mergers & acquisition deals or diplomatic relationships between governments. In practice, obtaining such information as early as possible is crucial. Hence, in this work, we predict the destinations of state and corporate aircraft on live data, while the targets are still in the air. We use machine learning algorithms to predict the area of landing up to 2 h in advance. We evaluate our approach on more than 500,000 flights during 2018 obtained from the OpenSky Network. Full article

The mitigation of aviation environmental effects is one of the key requirements for sustainable aviation growth. Among various mitigation strategies, Noise Abatement Departure Procedures (NADPs) are a popular and effective measure undertaken by several operators. However, a large variation in departure procedures is observed in real operations. This study demonstrates the use of OpenSky ADS-B departure data for comparison and quantification of the differences in trajectories and the resulting community noise impact between real-world operations and NADPs. Trajectory comparison is accomplished in order to gain insights into the similarity between NADPs and real-world procedures. Clustering algorithms are employed to identify representative departure procedures, enabling efficient high-fidelity noise modeling. Finally, noise results are compared in order to quantify the difference in environmental impacts arising from variability in real-world trajectories. The methodology developed enables more efficient and accurate environmental analyses, thereby laying the foundation for future impact assessment and mitigation efforts. Full article

Predictable operations are the basis of efficient air traffic management. In this context, accurately estimating the arrival time to the destination airport is fundamental to make tactical decisions about an optimal schedule of landing and take-off operations. In this paper, we evaluate different deep learning models based on LSTM architectures for predicting estimated time of arrival of commercial flights, mainly using surveillance data from OpenSky Network. We observed that the number of previous states of the flight used to make the prediction have great influence on the accuracy of the estimation, independently of the architecture. The best model, with an input sequence length of 50, has reported a MAE of 3.33 min and a RMSE of 5.42 min on the test set, with MAE values of 5.67 and 2.13 min 90 and 15 min before the end of the flight, respectively. Full article

Automatic Dependent Surveillance-Broadcast (ADS-B) enables aircraft to periodically broadcast their flight states such as position and velocity. Compared to classical radar surveillance, it increases update rate and accuracy. Currently, Mode S Extended Squitter is the most common implementation for ADS-B. Due to the simplicity of Mode S design, ADS-B signals are prone to injections. This study proposes a cost-effective solution that verifies the integrity of ADS-B signals using coherent receivers. We design the verification approach by combining the signal’s direction of arrival, estimated from the multi-channel data, with the target bearing calculated from ADS-B messages. By using another high-performance software-defined radio transceiver, we also conduct real signal injection experiments to validate our approaches. Full article

In this paper, we propose a data-driven approach that estimates cruise-level flight emissions over Europe using OpenSky ADS-B data and OpenAP emission models. Flight information, including position, altitude, speed, and the vertical rate are obtained from the OpenSky historical database, gathered at a sample rate of 15 s. Emissions from each flight are estimated at a 30-s time interval. This study makes use of the first four months of flights in 2020 over the major part of Europe. The dataset covers the period before and at the start of the COVID-19 pandemic. The aggregated results show cruise-level flight emissions by different airlines, geographic regions, altitudes, and timeframe (e.g., weeks). We also estimate environmental costs associated with aviation in Europe by using marginal cost values from the literature. Overall, we have demonstrated how open flight data from OpenSky can be employed to rapidly assess aviation emissions at varying spatio-temporal resolutions on a continental scale. Full article

The Metroplex Overflight Noise Analysis (MONA) project seeks to measure, analyze, and archive the ground noise generated by aircraft overflights and to provide accurate and actionable data for a variety of different purposes. On the one hand, experimental datasets collected and processed by the MONA system can serve as an openly-available database for validation and verification (V&V) of improved noise prediction methods. On the other, study conclusions derived from both the experimental and computational data can serve to inform technical discussions and options involving aircraft noise, aircraft routes, and the potential impacts of the FAA’s NextGen procedure changes on overflown communities at varying distances from the airport. Given the complex interdependencies between the noise levels perceived on the ground and the air-traffic patterns that generate the aircraft noise, a secondary goal of the MONA project is to share, through compelling visualizations, key results with broad communities of stakeholders to help generate a common understanding and reach better decisions more quickly. In this paper, we focus on the description of the MONA system architecture, its design, and its current set of capabilities. Subsequent publications will focus on the results we are obtaining though the use of the MONA system. Full article

Aircraft trajectory generation is a high stakes problem with a wide scope of applications, including collision risk estimation, capacity management and airspace design. Most generation methods focus on optimizing a criterion under constraints to find an optimal path, or on predicting aircraft trajectories. Nevertheless, little in the way of contribution has been made in the field of the artificial generation of random sets of trajectories. This work proposes a new approach to model two-dimensional flows in order to build realistic artificial flight paths. The method has the advantage of being highly intuitive and explainable. Experiments were conducted on go-arounds at Zurich Airport, and the quality of the generated trajectories was evaluated with respect their shape and statistical distribution. The last part of the study explores strategies to extend the work to non-regularly shaped trajectories. Full article

This document describes our pipeline for automatic processing of ATCO pilot audio communication we developed as part of the ATCO${}^2$ project. So far, we collected two thousand hours of audio recordings that we either preprocessed for the transcribers or used for semi-supervised training. Both methods of using the collected data can further improve our pipeline by retraining our models. The proposed automatic processing pipeline is a cascade of many standalone components: (a) segmentation, (b) volume control, (c) signal-to-noise ratio filtering, (d) diarization, (e) ‘speech-to-text’ (ASR) module, (f) English language detection, (g) call-sign code recognition, (h) ATCO—pilot classification and (i) highlighting commands and values. The key component of the pipeline is a speech-to-text transcription system that has to be trained with real-world ATC data; otherwise, the performance is poor. In order to further improve speech-to-text performance, we apply both semi-supervised training with our recordings and the contextual adaptation that uses a list of plausible callsigns from surveillance data as auxiliary information. Downstream NLP/NLU tasks are important from an application point of view. These application tasks need accurate models operating on top of the real speech-to-text output; thus, there is a need for more data too. Creating ATC data is the main aspiration of the ATCO${}^2$ project. At the end of the project, the data will be packaged and distributed by ELDA. Full article

Global modernization efforts focus on increasing aviation system capacity and efficiency, while maintaining high levels of safety. To accomplish these objectives, new analysis methods are required that consider Air Traffic Management (ATM) system operations at both the flight level and the airspace level. With the expansion of ADS-B technology, open-source flight tracking data has become more readily available to enable larger-scale analyses of aircraft operations. Specifically, anomaly detection has been identified as being paramount. However, previous analyses of airspace-level operational states have not considered the observation of transitional (transitioning between two distinct airspace-level operational patterns) or anomalous operational states. Therefore, a method is proposed in which the time-series trajectory data of all aircraft operating within a terminal airspace during a specified time period is aggregated to generate a representation of the airspace-level operational states such that a recursive DBSCAN procedure to characterize airspace-level operational states as either nominal, transitional, or anomalous as well as to identify the distinct nominal operational patterns. This method is demonstrated on one year of ADS-B trajectory data for aircraft arriving at San Francisco International Airport (KSFO). Overall, visual inspection of results indicate the method’s promise in assisting ATM system operators, decision-makers, and planners in designing the implementation of new operational concepts. Full article

Significant challenges exist when defining the usage spectra of helicopter components due to the wide range of missions and manoeuvres flown by helicopters in-service. Automatic Dependent Surveillance-Broadcast (ADS-B) trajectories provide a means of constructing helicopter flight manoeuvre statistics across entire in-service fleets. This paper explores the feasibility of characterising helicopter manoeuvres by applying rule-based algorithms to ADS-B trajectories from a fleet of twin-seat training helicopters. Despite challenges relating to low-altitude ADS-B coverage, a comprehensive set of flight manoeuvre statistics was generated, which highlighted that significant variability exists in helicopter flight manoeuvre occurrences. The generated statistics can also support validation activities concerning design usage spectra assumptions. Full article

The OpenSky ADS-B/Mode-S databases have been fully integrated into a computational model that is used to estimate aircraft and rotorcraft engine emissions. This paper demonstrates the basis for the method and the generalisation to a wide class of aircraft types. First, we use mathematical operations (filters and machine learning) to clean up the data and generate first-order derivatives. Then, we “fly” these trajectories using ancillary databases and numerical methods, including models for gas turbine engine emissions (CO${}_2$, CO, NO${}_x$, SO${}_x$, H${}_2$O, particulate matter). We show results for short-commuter flights (turboprop airplane), wide-body commercial aircraft, business jets, and helicopters. We demonstrate the main features, which include the ability to aggregate data depending on city-pairs, flight distance, and altitude distribution of emissions. Full article

In this paper, we present the first place solution for the Aircraft Localization Competition, which was held on the AIcrowd platform between 15 June 2020 and 31 January 2021 and was organized by the OpenSky Network and the Cyber-Defence Campus of armasuisse Science and Technology. The data for the competition was collected by the OpenSky Network from hundreds of crowdsourced low-cost receivers with nanosecond precision timestamps. Many receivers experienced clock drift and random walk and even provided fully broken timestamps. The solution combines well-known multilateration positioning with a variety of filtering methods and two tailored models for radio wave propagation and receiver clock drift to predict unknown aircraft locations. In this solution, we managed to synchronize 241 receivers, including 36 GPS-equipped, and achieved 81.9 m RMSE 2D distance prediction accuracy on $70\%$ of samples on the private leaderboard. Full article

With the development of aircraft equipment, conventional navigation shifted towards performance-based navigation (PBN) procedures, which significantly improved the efficiency of airport arrivals. Availability of the open-access Automatic Dependent Surveillance-Broadcast (ADS-B) data enables research targeting careful and detailed analysis of the arrival performance. In this work, we demonstrate how we use historical data provided via Opensky Network to investigate various aspects of arrival performance at three European airports implementing different sequencing and merging techniques (Dublin, Stockholm-Arlanda, and Vienna). We create a number of datasets of different size and features, and apply a set of performance metrics characterizing horizontal and vertical efficiency, sequencing and metering effort, as well as environmental efficiency at the chosen airports. Full article

Without a doubt, a publicly verifiable data is required to ensure a strong, transparent and independent air traffic management performance review system. Community sourced data (such as ADS-B/Mode S provided by OpenSky Network and others alike) has been used to analyse different aspects of air traffic management. The main drawback of such ADS-B data is the lack of crucial pieces of information that need to be inferred. On the other hand, Eurocontrol has used correlated position reports (CPRs) gathered from European Air Navigation Service Providers (ANSP) to conduct some of its actual/flown trajectory oriented performance analysis. The availability and the granularity of the CPRs vary between Eurocontrol Member States, making it difficult to perform accurate wide-scale studies. Using the strengths of both data sources would obviously result in great benefits. This paper describes the first step in creating a pan-European Flight Table (FT) and its supporting reference trajectories (RT). It is expected that the resulting dataset will be made available for the general public and that the work will continue to improve in scope and accuracy. Full article