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.
翻译:抗电合物,对于凝结路径来说,是飞机发动机在冷湿空气中飞行时产生的直形冰云,是飞机发动机在飞过冷湿空气时产生的成型的冰云,通过吸收或将大约33%的散射长波辐射带回地球而产生温室效应,占航空活动产生的气候变化的一半以上。避免抗电和调整飞行路线可能是减少其影响的一种廉价而有效的方法。需要一种准确、自动和可靠的检测算法来制定和评价抗电阻战略。由于缺少质量标签数据等若干因素,反电检测的进展受到严重限制。最近,提出了大型人类标记Landsat-8反电站数据集,每个反电源都与Landat-8卫星图象各种场上的各种投入仔细贴上标签。在这项工作中,我们把几种流行的分解模型与不同的损失功能和诱变码骨结合起来作为基准。这项工作首先应用最新分解技术来探测低轨道卫星图像中的反源。我们的工作也可以作为公开使用的公开基准。