Speaker
Agata Wijata
Description
Methane detection and monitoring are crucial for environmental surveillance and emissions management, as methane is a potent greenhouse gas with a global warming potential 28 times higher than carbon dioxide. This task poses practical challenges due to variable concentrations, irregular shapes of methane plumes, and the need for resource-frugal algorithms for onboard satellite processing, given the limited downlink bandwidth for large hyperspectral images. Machine learning (ML) methods—ranging from classical to deep and quantum approaches—offer global scalability for methane detection, but their deployment requires the ability to generalize to target data.
