Combining UPerNet and ConvNeXt for Contrails Identification to reduce
  Global Warming
Zhenkuan Wang
Semantic segmentation is a critical tool in computer vision, applied in
various domains like autonomous driving and medical imaging. This study focuses
on aircraft contrail detection in global satellite images to improve contrail
models and mitigate their impact on climate change.An innovative data
preprocessing technique for NOAA GOES-16 satellite images is developed, using
brightness temperature data from the infrared channel to create false-color
images, enhancing model perception. To tackle class imbalance, the training
dataset exclusively includes images with positive contrail labels.The model
selection is based on the UPerNet architecture, implemented using the
MMsegmentation library, with the integration of two ConvNeXt configurations for
improved performance. Cross-entropy loss with positive class weights enhances
contrail recognition. Fine-tuning employs the AdamW optimizer with a learning
rate of $2.5 \times 10^{-4}$.During inference, a multi-model prediction fusion
strategy and a contrail determination threshold of 0.75 yield a binary
prediction mask. RLE encoding is used for efficient prediction result
organization.The approach achieves exceptional results, boasting a high Dice
coefficient score, placing it in the top 5\% of participating teams. This
underscores the innovative nature of the segmentation model and its potential
for enhanced contrail recognition in satellite imagery.For further exploration,
the code and models are available on GitHub:
\url{https://github.com/biluko/2023GRIC.git}.