mlsanityData to 7 Oct 2026

Optimizing Contrail Detection: A Deep Learning Approach with EfficientNet-b4 Encoding

arXiv 2404.14441 ↗First posted 20 Apr 2024cs.CV cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 36 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

13 of 36 references.

References · 13

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1 more
  1. 2024Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Methodcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Towards Airborne Object Detection: A Deep Learning Analysiscited by 0