<ul><li>Operational deployment of automated greenhouse gas plume detection system is challenging despite advances in deep learning.</li><li>Key obstacles addressed include data quality control, prevention of biases, and aligned modeling objectives.</li><li>Convolutional neural networks show promising performance in detecting GHG plumes when obstacles are mitigated.</li><li>A multitask model for instance detection and segmentation can pave the way towards operational deployment, with defined thresholds and best practices provided.</li></ul>

Towards Operational Automated Greenhouse Gas Plume Detection

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