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https://api.rohub.org/api/ros/7435ba71-48f2-4475-999e-cd818cc941ce/crate/download/ https://w3id.org/ro-id/7435ba71-48f2-4475-999e-cd818cc941ce/ http://schema.org/creator mailto:bernhard.raml@tuwien.ac.at https://w3id.org/ro-id/7435ba71-48f2-4475-999e-cd818cc941ce/ http://schema.org/dateCreated 2023-07-12 07:20:55.783495+00:00 https://w3id.org/ro-id/7435ba71-48f2-4475-999e-cd818cc941ce/ http://schema.org/dateModified 2025-03-05 00:50:04.115964+00:00 https://w3id.org/ro-id/7435ba71-48f2-4475-999e-cd818cc941ce/ http://schema.org/datePublished 2023-07-12 07:20:55.783495+00:00 https://w3id.org/ro-id/7435ba71-48f2-4475-999e-cd818cc941ce/ http://schema.org/description With this pipeline we aim to provide users with the ability to train spatiotemporally robust machine learning models to detect and monitor wetlands and thus assessing their state over time. Wetlands play a vital role in the ecosystem, but also have critical influence on methane emissions. Methane is around 25 times as powerful in trapping heat in the atmosphere, but because it does not stay in the atmosphere as long, it more has a short-term influence on the rate of climate change. See also this news release by NOAA for more details. 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