Google and the UK government are collaborating on a project to test whether airplanes can avoid creating contrails. The project, called Operation Blue Skies, will conduct the trial over Shanwick airspace, which sits above the northeastern Atlantic Ocean. Contrails are the long white streaks in the sky left by jet engines. Most often created in cold, humid air, they form when water vapor condenses and freezes around aircraft exhaust. Researchers estimate that they contribute to about one-third of aviation's total climate impact.

AI to help planes avoid climate-warming'sky graffiti' We've all looked up and seen the white lines trailing behind aircraft in the sky. Known as contrails, these icy clouds contribute to climate change, and scientists have been trying to work out how to stop them. Now, a £5m UK trial will test whether AI can help, by predicting where warming contrails are likely to form and getting planes to avoid those areas. The 30-month Operation Blue Skies project brings together Google, the UK government, the Met Office, air traffic control provider National Air Traffic Services (NATS) and academic researchers. It will focus on the Shanwick Oceanic airspace in the eastern half of the North Atlantic corridor which accounts for approximately 5% of global contrail warming.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes rerouted through these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes rerouted through these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

A trial involving thousands of flights between the US and Europe has found that planes produce fewer contrails if they follow flight paths recommended by an artificial intelligence to reduce their global warming impact. The streaks of condensation triggered by soot particles produced by aircraft engines are thought to cause more warming than the carbon dioxide that planes emit. Research has also shown that some ice-rich regions of the upper atmosphere are more likely to form contrails when a plane passes through them, and that AI can predict where these regions will be using detailed weather forecasts. We're finally solving the puzzle of how clouds will affect our climate There have been small-scale trials showing that planes bypassing these regions will produce fewer contrails, but the practice has yet to be applied to commercial flights at scale. Now, Dinesh Sanekommu at Google and his colleagues have used an AI contrail-forecasting tool to give routing advice in a randomised control trial of more than 2400 real American Airlines flights.

Aviation's climate impact includes not only CO2 emissions but also significant non-CO2 effects, especially from contrails. These ice clouds can alter Earth's radiative balance, potentially rivaling the warming effect of aviation CO2. Physics-based models provide useful estimates of contrail formation and climate impact, but their accuracy depends heavily on the quality of atmospheric input data and on assumptions used to represent complex processes like ice particle formation and humidity-driven persistence. Observational data from remote sensors, such as satellites and ground cameras, could be used to validate and calibrate these models. However, existing datasets don't explore all aspect of contrail dynamics and formation: they typically lack temporal tracking, and do not attribute contrails to their source flights. To address these limitations, we present the Ground Visible Camera Contrail Sequences (GVCCS), a new open data set of contrails recorded with a ground-based all-sky camera in the visible range. Each contrail is individually labeled and tracked over time, allowing a detailed analysis of its lifecycle. The dataset contains 122 video sequences (24,228 frames) and includes flight identifiers for contrails that form above the camera. As reference, we also propose a unified deep learning framework for contrail analysis using a panoptic segmentation model that performs semantic segmentation (contrail pixel identification), instance segmentation (individual contrail separation), and temporal tracking in a single architecture. By providing high-quality, temporally resolved annotations and a benchmark for model evaluation, our work supports improved contrail monitoring and will facilitate better calibration of physical models. This sets the groundwork for more accurate climate impact understanding and assessments.

Deep Learning became an ubiquitous paradigm due to its extraordinary effectiveness and applicability in numerous domains. However, the approach suffers from the high demand of data required to achieve the potential of this type of model. An ever-increasing sub-field of Artificial Intelligence, Image Synthesis, aims to address this limitation through the design of intelligent models capable of creating original and realistic images, endeavour which could drastically reduce the need for real data. The Stable Diffusion generation paradigm recently propelled state-of-the-art approaches to exceed all previous benchmarks. In this work, we propose the ContRail framework based on the novel Stable Diffusion model ControlNet, which we empower through a multi-modal conditioning method. We experiment with the task of synthetic railway image generation, where we improve the performance in rail-specific tasks, such as rail semantic segmentation by enriching the dataset with realistic synthetic images.

Aeroplanes that fly at higher altitudes can create longer-lasting vapour trails that are likely to cause more global warming. Since private jets and modern fuel-efficient jets fly higher than other passenger jets, these aircraft may be causing even more warming than previously thought. The findings could help airlines work out which routes to fly to minimise contrails, says Edward Gryspeerdt at Imperial College London. "If we could predict the contrail-forming regions of the atmosphere well enough, you could route aircraft around them, which would reduce this effect." Aircraft contrails are a climate menace.

Before the start of India's general election in April, a top candidate looking to unseat Prime Minister Narendra Modi was not out wooing voters on the campaign trail. Arvind Kejriwal, the chief minister of Delhi and the head of a political party known for its anti-corruption platform, was arrested in late March for, yes, alleged corruption. His supporters hit the streets in protest, decrying the arrest as a politically motivated move by Modi aimed at weakening a rival. Soon after the arrest, Kejriwal implored his supporters to stay strong. "There are some forces who are trying to weaken our country and its democracy," he said in a 34-second audio clip posted to social media by a fellow party member.

In the pursuit of environmental sustainability, the aviation industry faces the challenge of minimizing its ecological footprint. Among the key solutions is contrail avoidance, targeting the linear ice-crystal clouds produced by aircraft exhaust. These contrails exacerbate global warming by trapping atmospheric heat, necessitating precise segmentation and comprehensive analysis of contrail images to gauge their environmental impact. However, this segmentation task is complex due to the varying appearances of contrails under different atmospheric conditions and potential misalignment issues in predictive modeling. This paper presents an innovative deep-learning approach utilizing the efficient net-b4 encoder for feature extraction, seamlessly integrating misalignment correction, soft labeling, and pseudo-labeling techniques to enhance the accuracy and efficiency of contrail detection in satellite imagery. The proposed methodology aims to redefine contrail image analysis and contribute to the objectives of sustainable aviation by providing a robust framework for precise contrail detection and analysis in satellite imagery, thus aiding in the mitigation of aviation's environmental impact.