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Workshop: Tackling Climate Change with Machine Learning

Performance evaluation of deep segmentation models on Landsat-8 imagery

Akshat Bhandari · Pratinav Seth · Sriya Rallabandi · Aditya Kasliwal · Sanchit Singhal


Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through the 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-labelled data. Recently, McCloskey et al. proposed a large human-labelled Landsat-8. Each contrail is carefully labelled 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.

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