Condensation trails, or contrails, are line-shaped clouds that are produced by an aircraft engine exhaust. These contrails often impact climate significantly due to their potential warming effect. Identification of contrail formation through satellite images has been an ongoing research challenge. Traditional computer vision techniques struggle with varying imagery conditions, and supervised machine learning approaches often require a large amount of hand-labeled images. This study researches few-shot transfer learning and provides an innovative approach for contrail segmentation with a few labeled images. The methodology leverages backbone segmentation models, which are pre-trained on existing image datasets and finetuned using an augmented contrail-specific dataset. We also introduce a new loss function, SR Loss, which enhances contrail line detection by incorporating Hough transformation in model training. This transformation improves performance over generic image segmentation loss functions. The openly shared few-shot learning library, contrail-net, has demonstrated that few-shot learning can be effectively applied to contrail segmentation with the new loss function.

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Estimating contrail formation and proposing effective mitigation strategies have posed significant challenges for the aviation industry in recent years. This study utilizes the Japanese airspace as a case study to address the challenge of assessing and minimizing the environmental impact of contrails. Initially, we introduce a novel combination of meteorological and flight trajectory data sources, followed by a comparative data quality analysis. Drawing on four months of data during different seasons from 2023, we conduct an in-depth analysis of contrail formation within the Japanese airspace, uniquely quantifying contrails with highresolution data to provide insights into their geographical and seasonal variations. Subsequently, we examine the effectiveness of altitude diversions as a mitigation strategy. Our findings identify clear geographical and seasonal influences on contrail formation in the region. We illustrate that altitude diversions can significantly reduce contrail formation with minimal altitude adjustments of up to 2000 ft. We found that minor altitude diversions can mitigate between 70% and 90% of the persistent contrail formed near the Japan region. Notably, the results also highlight a concerning phenomenon: during warmer months, such as July, a higher quantity and percentage of persistent contrails is observed, and a larger proportion of these contrails cannot be mitigated through altitude diversions. This result could intensify positive radiative forcing during warmer periods, underscoring the need for further research into contrail mitigation strategies. @en

Contrails, formed under specific atmospheric conditions, have a noteworthy role in heat-trapping within the atmosphere. This study bridges the gap between theoretical contrail formation models and real-world data by employing flight information from OpenSky and meteorological data from the European Centre for Medium-Range Weather Forecasts. We introduce a computationally efficient contrail estimation module, leveraging a client-server architecture that allows on-demand weather data interpolation via an API, significantly reducing computational load and enhancing performance locally. The study also benchmarks the entire pipeline, from data acquisition to contrail prediction, offering a robust tool for future air traffic studies requiring interpolated weather data.@en

Contrails, formed under specific atmospheric conditions, have a noteworthy role in heat-trapping within the atmosphere. This study bridges the gap between theoretical contrail formation models and real-world data by employing flight information from OpenSky and meteorological data from the European Centre for Medium-Range Weather Forecasts. We introduce a computationally efficient contrail estimation module, leveraging a client-server architecture that allows on-demand weather data interpolation via an API, significantly reducing computational load and enhancing performance locally. The study also benchmarks the entire pipeline, from data acquisition to contrail prediction, offering a robust tool for future air traffic studies requiring interpolated weather data.@en

Emissions and contrails are key factors in aviationinduced climate change, often presenting conflicting objectives in flight trajectory optimization. Previous research typically lacks in optimizer efficiency or in addressing these trade-offs, with limited use of extensive meteorological and flight data. In this paper, a fully open non-linear optimal control flight optimization approach is designed for contrail avoidance and emission reduction, with high computational efficiency. This is achieved by leveraging the most recent trajectory optimizer, OpenAP.TOP, and atmospheric data handling tool, fastmeteo. We present a new compound grid-based objective function that considers both contrails and emissions and introduce four different metrics for evaluating the performance. A total of four months’ worth of data, containing around half a million flights, are gathered from OpenSky for analyses. We show that high levels of contrail mitigation can be achieved, without significantly increasing flight time, distance, or emissions. @en

With the increasing coverage of the OpenSky, flight data gathered from the network of receivers has become a primary source of open data for aviation research. This year marks ten years of OpenSky. In this report, we employ a year's worth of OpenSky data to analyze the formation of contrails and study the potential mitigation with altitude diversions. Persistent contrails, often formed under humid atmospheric conditions, significantly trap outgoing terrestrial radiation. More insights into contrails are essential for studying aviation climate impact. We estimated the potential formation of persistent contrails based on the numerical weather assimilation data. An efficient approach is employed to fuse meteorology data in our analysis, which allows the fast evaluation of these contrail conditions at a very large scale. We designed a simple yet effective algorithm for flight with persistent contrails to study the shortest altitude diversion that would have prevented the contrail formations. We have estimated that between 5 % and 9 % of total flight distances each month could have formed persistent contrails. Furthermore, altitude diversions could have mitigated 70 % of these contrails.

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Current advancements in machine learning have provided new architectures, such as encoder-decoder transformers, for automatic speech recognition. For generic speech recognition, very high accuracies are already achievable. However, in air traffic control, automatic speech recognition models traditionally rely on domain-specific models constructed from limited training data. This study introduces this newly developed transformer model for air traffic control and provides a set of fully open automatic speech recognition models with high accuracies. This paper demonstrates how a large-scale, weakly supervised automatic speech recognition model, Whisper, is fine-tuned with various air traffic control datasets to improve model performance. We also evaluated the performance of different sizes of Whisper models. In the end, it was possible to achieve word error rates of 13.5% on the ATCO2 dataset and 1.17% on the ATCOSIM dataset with a random split (or 3.88% with speaker split). The study also reveals that finetuning with region-specific data can enhance performance by up to 60% in real-world scenarios. Finally, we have open-sourced the code base and the models for future research. @en

Air traffic sector demand and capacity balancing are necessary for safe and efficient flight execution. Demand and capacity are determined in current operations based on schedules and flight plans. This research aims to improve air traffic demand forecasting by exploring machine learning-based trajectory prediction, specifically the newly emerged transformer-based neural network models. The predicted trajectories are considered to improve demand forecasts for Air Traffic Control in the Netherlands. We successfully built a transformer neural network using available traffic messages from the EuroControl B2B connection and actual trajectories obtained from the OpenSky ADS-B repository. A new lost function is specifically designed to improve this prediction model’s performance. This trajectory predictor could accurately generate trajectories, outperforming the flight plan and other neural network approaches by a good margin. For demand prediction, introducing improved trajectories provided small gains that could lead to more stable predictions. @en

The study of the environmental transition of the aviation sector calls for prospective traffic scenarios. Detailed traffic and emissions inventories are often needed to refine the available analyses and to enable the simulation of regionalised scenarios. In the past studies, these are generally based on commercial, proprietary traffic data, making their dissemination problematic and reducing the reproducibility of the science produced. Open-source alternatives do exist, but with limited geographical coverage. This paper presents a method to aggregate different sources of flight information, in order to obtain an open-source air traffic dataset for 2019. Then, missing flight information is identified and completed using an airline route database built from Wikipedia parsing and related socio-economic data. After that, several reference datasets are used to evaluate the accuracy of the extended open-source dataset. Despite varying accuracy for different routes, major traffic flows are reasonably well estimated at the country and continental levels. Finally, the CO2 emissions are obtained using an existing aircraft performance surrogate model, and the accuracies are examined compared to the results from previous studies. @en

Predicting aircraft Take-Off Weight (TOW) has been a long-standing goal for aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Accurate TOW values would enable better emissions computation, leading to more effective regulation of aviation’s climate impact. However, aircraft operators prefer to keep TOWs confidential because they are sensitive to operational trends and cost indices. Consequently, many works have attempted to circumvent this gap by predicting TOW values. Unfortunately, limited success has been achieved primarily due to the lack of accurate real-world operational data. This study is unique in utilizing operational TOW data provided by airlines. We predict TOW before takeoff based solely on Flight Plan and Terminal Aerodrome Forecast parameters, primarily focusing on flights at Amsterdam Airport Schiphol. The accuracy of several Machine Learning algorithms is directly compared. The best Mean Absolute Percentage Error of 2.17% on the Schiphol testing dataset is achieved. The model is further validated on flights at Paris- Charles de Gaulle Airport and Brussels South Charleroi Airport with errors of 4.07% and 3.41%. We found that the distribution of flights in the training dataset, particularly aircraft and airline types, significantly influenced the model’s applicability. Recommendations are also made on how to improve the model further.@en

The study of the environmental transition of the aviation sector calls for prospective traffic scenarios. Detailed traffic and emissions inventories are often needed to refine the available analyses and to enable the simulation of regionalised scenarios. In the past studies, these are generally based on commercial, proprietary traffic data, making their dissemination problematic and reducing the reproducibility of the science produced. Open-source alternatives do exist but with limited geographical coverage. This study bridges this gap by presenting an innovative open-source dataset detailing 2019's global air traffic flows and associated CO2 emissions. A comprehensive approach that compiles diverse flight data sources is presented. The remaining data gaps are addressed by constructing a route network through systematic Wikipedia parsing and by estimating the related traffic using socio-economic data. Then, an aircraft performance model to estimate CO2 emissions is implemented. This methodology promises reinforced reproducibility and broader data accessibility in aviation environmental research. Several reference datasets are used to evaluate the accuracy of the open-source dataset. Despite various levels of accuracy for individual routes, major traffic flows are well estimated at the country and continental levels, albeit with room for refinement to ensure consistent data reliability. To facilitate the exploration of the dataset, the AeroSCOPE tool has been developed. To initiate research prospects, use cases of this dataset are proposed, concerning the network potential of electric and hydrogen-powered aircraft and inequalities in air transport.@en

As global flight volume rises, the aviation industry is facing increasing climate challenges. One major factor is the impact of contrails, which trap outgoing terrestrial radiation and counteract emission reduction benefits from emission-optimized flight routes. Our study quantifies contrail-forming flights globally and assesses altitude adjustments necessary to avoid these regions. Using the Integrated Global Radiosonde Archive and global flight data from 2021-2022, we highlight several contrail-prone regions with high air traffic volumes and high potential for contrail-formation. We propose an operational strategy in altitude diversion, which can halve the amount of persistent contrails. Further, we analyse the additional carbon emissions caused by the altitude diversions and safety risks in terms of potential new conflicts. Our findings provide actionable strategies for policymakers to balance climate mitigation and operational challenges in aviation.

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This work presents 10 rules that provide guidance and recommendations on how to start up discussions around the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and creation of standardised ways of working. These recommendations will be particularly relevant if you are unsure where to start, who to involve, what the benefits and barriers of standardisation are, and if little work has been done in your discipline to standardise research workflows. When applied, these rules will support a more effective way of engaging the community with discussions on standardisation and practical implementation of the FAIR principles.

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In this paper, we analyze two months of trajectory data for aircraft landing in five major European airports. Based on open ADS-B data from the OpenSky Network and open performance models, we enrich all trajectories with automatically detected procedure information, fuel consumption, and emissions for supported aircraft types. To assess the inefficiencies associated with holding patterns, point merges, and continuous descent operations across different airports, we propose methodologies to quantify and compare these environmental inefficiencies. Holding patterns are found to have a higher negative impact on the environment than point merge and continuous descent operations. Furthermore, the paper provides recommendations for procedure evaluations of future airports, which could help policymakers and relevant stakeholders to evaluate the environmental performances of arrival procedures based on open data and open models@en

This paper addresses the problem of spatiotemporal wind velocity field estimation for air traffic management applications. Using data obtained from aircraft, the eastward and northward components of the wind velocity field inside a specific air space are calculated as functions of time. Both short-term wind velocity field forecasting and wind velocity field reconstruction are performed. Wind velocity data are indirectly obtained from the states of the aircraft flying in the relevant airspace, which are broadcast by the ADS-B and Mode-S aircraft surveillance systems. The wind velocity field is estimated by combining two data-driven techniques: the polynomial chaos expansion and the Gaussian process regression. The former approximates the global behavior of the wind velocity field, whereas the latter approximates the local behavior. The eastward and northward wind components of the wind velocity field must be estimated, which causes the problem to be a multiple-output problem. This method enables the estimation of the wind velocity field at any spatiotemporal location using wind velocity observations from any spatiotemporal location, eliminating the need for spatial and temporal grids. Moreover, since the method proposed in this article allows for the probability distributions of the estimates to be computed, it causes the computation of the confidence intervals to be possible. Furthermore, since the method presented in this paper allows for data assimilation, it can be used online to continuously update the wind velocity field estimation. The method is tested on different wind scenarios and different training-test data configurations, by means of which the consistency between the results of the wind velocity field forecasting and the wind velocity field reconstruction is checked. Finally, the ERA5 meteorological reanalysis data of the European Centre for Medium-Range Weather Forecasts are used to validate the proposed technique. The results show that the method is able to reliably estimate the wind velocity field from aircraft-derived data.

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Contrails contribute to global warming by trapping outgoing terrestrial radiation, exerting an immediate warming influence on the climate. The climatic impact of contrails is potentially comparable to that of aviation’s carbon emissions. This underlines the importance of minimizing contrail formation to mitigate the climate effects of aviation, both now and in the future. The evaluation of contrails demands more precise data on the location and altitude where they form. Remote sensing imagery enables the identification of their location. Nevertheless, determining the altitude of the contrail remains problematic, complicating the identification of the source flight. This study introduces a novel method that enables researchers to determine the altitude of a contrail solely using Landsat data by analysing shadows cast by contrails. Through validation against ADS-B data from OpenSky, we demonstrate that such a technique can achieve the accuracy of a few hundreds of meters, which is suitable for incorporation into a climate-optimized routing system. Finally, a ResUNet segmentation model is also presented, which can identify contrails and their shadows in Landsat imagery. These results constitute a step forward for more accurate contrail dataset and models. @en

The increasing demand for global air travel has intensified the urgency to mitigate aviation’s carbon emissions. Continuous monitoring of aircraft fuel efficiency and emissions has become an important task in aviation. One of the main challenges has been the lack of surveillance data for flights across oceans, specifically in the North Atlantic region, where numerous flights occur. Recently, space-based ADS-B data has been made available by new space companies like Spire Global, enabling flight surveillance for aircraft in remote regions, including transatlantic flights. In this study, we utilize several months of space-based ADS-B data from Spire, combined with groundbased ADS-B data from the OpenSky Network, to demonstrate increased accuracy in flight trajectory and emission estimations. We introduce the use of wind data to improve emission quantification. Utilizing these accurate trajectories, we quantify excess emissions by comparing actual flight paths with their optimal alternatives. Our approach provides a robust methodology that benefits future policy for carbon emissions assessments. @en

Established in 2013, The OpenSky Network, a crowdsourced network of ADS-B receivers, has consistently collected surveillance data from equipped aircraft and made it available for science. Coverage has steadily improved, boasting more than 6000 registered sensors worldwide today. This platform has aided numerous researchers in publishing studies across fields such as air traffic management, security, environment, and radio frequency interference. Following the 2020 mandate, most aircraft flying at high altitudes in Europe or Northern America are within range of one of the network’s ADS-B receivers. To complement existing research using OpenSky data, this paper focuses on lower altitude coverage, including light aircraft, general aviation, gliders, and ultralights, which are not required to carry ADS-B transponders. Instead, these often use, esp. in Europe, another traffic awareness and collision avoidance technology known as FLARM. The OpenSky Network has been gathering FLARM messages since 2018, and now enough data is available for a detailed analysis. The aim of this report is to present OpenSky’s FLARM data, explain the workings of the technology, and highlight potential uses of this data for future research.@en

Wind velocity field knowledge is crucial for the future air traffic management paradigm and is key in many applications, such as aircraft performance studies. This paper addresses the problem of spatio-temporal windc velocity field estimation. The north and east wind components within a given air space are estimated as a function of time. Both wind velocity field reconstruction in space for a past or present time instant and short-term prediction are performed. Wind data are obtained indirectly from the states of the aircraft broadcast by the Mode-S and ADS-B aircraft surveillance systems. The Gaussian process regression method, which is a flexible and universal estimator, is employed to solve both problems. Under general conditions, the method is statistically consistent, meaning that the method converges to the ground truth when increasingly more data are available, which is especially interesting, since aircraft data availability is expected to grow in the future through the deployment of the European System-Wide Information Management. Besides estimation, the Gaussian process regression method provides the probability distribution of any particular estimate, allowing confidence intervals to be computed. Moreover, the spatial modelling is performed using raw data without relying on grids and estimation can be performed at any spatio-temporal location. Furthermore, since the training phase of the method described in this paper is fast, requiring less than 5 minutes on a standard desktop computer, it can be used online to continuously track the state of the wind velocity field, thus allowing for data assimilation. In the case study presented in this paper, the Gaussian process regression method is tested on different days with different wind intensities. The available data set is split into several training and testing data sets, which are used to check the consistency of the results of wind velocity field reconstruction and prediction. Finally, the Gaussian process regression method is validated using the European Centre for Medium-Range Weather Forecasts ERA5 meteorological reanalysis data. The obtained results show that Gaussian process regression can be used to reliably estimate the wind velocity field from aircraft derived data.@en