{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/29881"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/29881","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"PyrSat - Prevention and response to wild fires with an intelligent Earth observation CubeSat","abstract":"Forest fires are a pervasive and serious problem. Besides loss of life and extensive environmental damage, fires also result in substantial economic losses, not to mention property damage, injuries, displacements and hardships experienced by the affected citizens. This project proposes a low-cost intelligent hyperspectral 3U CubeSat for the production of fire risk and burnt area maps. It applies Machine Learning algorithms to autonomously process images and obtain final data products on-board the satellite for direct transmission to users on the ground. Used in combination with other services such as EFFIS or AFIS, the system could considerably reduce the extent and consequences of forest fires.","abstract_html":"Forest fires are a pervasive and serious problem. Besides loss of life and extensive environmental damage, fires also result in substantial economic losses, not to mention property damage, injuries, displacements and hardships experienced by the affected citizens. This project proposes a low-cost intelligent hyperspectral 3U CubeSat for the production of fire risk and burnt area maps. It applies Machine Learning algorithms to autonomously process images and obtain final data products on-board the satellite for direct transmission to users on the ground. 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Besides loss of life and extensive environmental damage, fires also result in substantial economic losses, not to mention property damage, injuries, displacements and hardships experienced by the affected citizens. This project proposes a low-cost intelligent hyperspectral 3U CubeSat for the production of fire risk and burnt area maps. It applies Machine Learning algorithms to autonomously process images and obtain final data products on-board the satellite for direct transmission to users on the ground. Used in combination with other services such as EFFIS or AFIS, the system could considerably reduce the extent and consequences of forest fires."]},{"key":"dc:title","label":"Title","values":["PyrSat - Prevention and response to wild fires with an intelligent Earth observation CubeSat"]}]}],"canonical_facts":{"dc:contributor.advisor":["Martinez, Peter"],"dc:creator":["Estebanez, Camarena Monica"],"dc:date.accessioned":["2019-03-01T09:16:24Z"],"dc:date.available":["2019-03-01T09:16:24Z"],"dc:date.issued":["2018"],"dc:description.abstract":["Forest fires are a pervasive and serious problem. Besides loss of life and extensive environmental damage, fires also result in substantial economic losses, not to mention property damage, injuries, displacements and hardships experienced by the affected citizens. This project proposes a low-cost intelligent hyperspectral 3U CubeSat for the production of fire risk and burnt area maps. 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