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.

How it works: Jets release heat, water vapor, and particulate matter that can produce thin clouds in the sky, known as "contrails". When numerous flights pass through such areas, these contrails can form clouds that absorb radiation escaping from the surface, acting as blankets floating above the Earth. Why it matters: A small fraction of overall flights, between 2% and 10%, create about 80% of the contrails. So the growing hope is that simply rerouting those flights could significantly reduce the effect, presenting a potentially high leverage, low cost and fast way of easing warming. The news: Since their inception, it's been clear that large language models like ChatGPT absorb racist views from the millions of pages of the internet they are trained on. Developers have responded by trying to make them less toxic.

Last summer, Breakthrough Energy, Google Research, and American Airlines announced some promising results from a research collaboration, as first reported in the New York Times. They employed satellite imagery, weather data, software models, and AI prediction tools to steer pilots over or under areas where their planes would be likely to produce contrails. American Airlines used these tools in 70 test flights over six months, and subsequent satellite data indicated that they reduced the total length of contrails by 54%, relative to flights that weren't rerouted. There would, of course, be costs to implementing such a strategy. It generally requires more fuel to steer clear of these areas, which also means the flights would produce more greenhouse-gas emissions (more on that wrinkle in a moment).

Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activities. Avoiding contrails and adjusting flight routes could be an inexpensive and effective way to reduce their impact. An accurate, automated, and reliable detection algorithm is required to develop and evaluate contrail avoidance strategies. Advancement in contrail detection has been severely limited due to several factors, primarily due to a lack of quality-labeled data. Recently, proposed a large human-labeled Landsat-8 contrails dataset. Each contrail is carefully labeled with various inputs in various scenes of Landsat-8 satellite imagery. In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones. This work is the first to apply state-of-the-art segmentation techniques to detect contrails in low-orbit satellite imagery. Our work can also be used as an open benchmark for contrail segmentation and is publicly available.

Air transport poses significant environmental challenges, particularly regarding the role of flight contrails in climate change due to their potential global warming impact. Traditional computer vision techniques struggle under varying remote sensing image conditions, and conventional machine learning approaches using convolutional neural networks are limited by the scarcity of hand-labeled contrail datasets. To address these issues, we employ few-shot transfer learning to introduce an innovative approach for accurate contrail segmentation with minimal labeled data. Our methodology leverages backbone segmentation models pre-trained on extensive image datasets and fine-tuned using an augmented contrail-specific dataset. We also introduce a novel loss function, termed SR Loss, which enhances contrail line detection by transforming the image space into Hough space. This transformation results in a significant performance improvement over generic image segmentation loss functions. Our approach offers a robust solution to the challenges posed by limited labeled data and significantly advances the state of contrail detection models.

Artificial intelligence is either going to save humanity or finish it off, depending on who you speak to. Either way, every week there are new developments and breakthroughs. "Just accept the tech, architects!" Oliver Wainwright, our architecture and design critic, looks at whether AI will wipe out architects. Teaser: it can quickly show you what mosques in Abu Dhabi could look like, summarises local planning policies and allows the public to experiment with projects. If architects want to explore the endless world of AI, they can start by viewing AI as their perfectly on-time, organised and eager studio assistant.