Mask R-CNN with ResNet-50 pre-trained on MethaneAIR and fine-tuned on MethaneSAT, plus physics-informed postprocessing, yields instance-level precision 0.60/recall 0.98 at baseline, improving to 0.71/0.94 and 0.92/0.70 in two operational modes.
Ai for op- erational methane emitter monitoring from space.arXiv preprint arXiv:2408.04745, 2024
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Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing
Mask R-CNN with ResNet-50 pre-trained on MethaneAIR and fine-tuned on MethaneSAT, plus physics-informed postprocessing, yields instance-level precision 0.60/recall 0.98 at baseline, improving to 0.71/0.94 and 0.92/0.70 in two operational modes.