Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space

Air transport poses significant environmental challenges, particularly the contribution of flight contrails to climate change due to their potential global warming impact. Detecting contrails from satellite images has been a long-standing challenge. Traditional computer vision techniques have limitations under varying image conditions, and machine learning approaches using typical convolutional neural networks are hindered by the scarcity of hand-labeled contrail datasets and contrail-tailored learning processes. In this paper, we introduce an innovative model based on augmented transfer learning that accurately detects contrails with minimal data. We also propose a novel loss function, SR Loss, which improves contrail line detection by transforming the image space into Hough space. Our research opens new avenues for machine learning-based contrail detection in aviation research, offering solutions to the lack of large hand-labeled datasets, and significantly enhancing contrail detection models.Comment: Source code available at: https://github.com/junzis/contrail-ne

Verification for Different Contrail Parameterizations Based on Integrated Satellite Observation and ECMWF Reanalysis Data

Aviation induced cloud termed contrail plays a more and more important role in the climate change, which makes a significant contribution to anthropogenic climate forcing through impacting the coverage of cirrus in the intersection of troposphere and stratosphere. In this paper, we propose one novel automatic contrail detecting method based on Himawari-8 stationary satellite imagery and two kinds of potential contrail coverage (PCC1 and PCC2) from contrail parameterization in ECHAM4 and HadGEM2. In addition, we propose one new climatological index called contrail occurrence and persistence (COP). According to the algorithm identification (AI) and artificial visual inspection (AVI), COP measured from Himawari-8 stationary satellite imagery is related to upper tropospheric relative humidity over ice (RHI) computed with the ECMWF reanalysis data by simple linear regression. Similarly, we compared the linear correlation between COP and PCCs fractions and found that PCC1 has better correspondence with COP than PCC2

Automated ATM system enabling 4DT-based operations

As part of the current initiatives aimed at enhancing safety, efficiency and environmental sustainability of aviation, a significant improvement in the efficiency of aircraft operations is currently pursued. Innovative Communication, Navigation, Surveillance and Air Traffic Management (CNS/ATM) technologies and operational concepts are being developed to achieve the ambitious goals for efficiency and environmental sustainability set by national and international aviation organizations. These technological and operational innovations will be ultimately enabled by the introduction of novel CNS/ATM and Avionics (CNS+A) systems, featuring higher levels of automation. A core feature of such systems consists in the real-time multi-objective optimization of flight trajectories, incorporating all the operational, economic and environmental aspects of the aircraft mission. This article describes the conceptual design of an innovative ground-based Air Traffic Management (ATM) system featuring automated 4-Dimensional Trajectory (4DT) functionalities. The 4DT planning capability is based on the multi-objective optimization of 4DT intents. After summarizing the concept of operations, the top-level system architecture and the key 4DT optimization modules, we discuss the segmentation algorithm to obtain flyable and concisely described 4DT. Simulation case studies in representative scenarios show that the adopted algorithms generate solutions consistently within the timeframe of online tactical rerouting tasks, meeting the set design requirements

Aircraft and Ship Velocity Determination in Sentinel-2 Multispectral Images

The Sentinel-2 satellites in the Copernicus program provide high resolution multispectral images, which are recorded with temporal offsets up to 2.6 s. Moving aircrafts and ships are therefore observed at different positions due to the multispectral band offsets, from which velocities can be determined. We describe an algorithm for detecting aircrafts and ships, and determining their speed, heading, position, length, etc. Aircraft velocities are also affected by the parallax effect and jet streams, and we show how the altitude and the jet stream speed can be determined from the geometry of the aircraft and/or contrail heading. Ship speeds are more difficult to determine as wakes affect the average ship positions differently in the various multispectral bands, and more advanced corrections methods are shown to improve the velocity determination