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SUMMARY:Remote sensing and machine learning for environmental mapping
DTSTART:20260213T130000Z
DTEND:20260213T170000Z
DTSTAMP:20260316T113900Z
UID:indico-event-1313@events.gwdg.de
CONTACT:kmeyer5@uni-goettingen.de
DESCRIPTION:Speakers: Hanna Meyer (University of Münster)\n\n!Course is f
 ully booked!\n \nOne key task in environmental science is to map environm
 ental variables continuously in space or even in space and time as a basel
 ine to inform decision-making in various applications\, such as agricultur
 e\, land-use planning\, or natural resource management\; or to study ecolo
 gical research questions based on spatial patterns. However\, most ecologi
 cal variables are only available as point data\, e.g. from field surveys. 
 Modelling approaches are hence required to move from local field observati
 ons to continuous maps of ecological variables by estimating the value of 
 the variable of interest in places where it has not been measured. \n \n
 In recent years\, machine learning methods have become a popular tool to l
 earn patterns in nonlinear and complex systems. They have been applied to 
 map various ecological variables\, even ambitiously on a global scale\, su
 ch as land cover\, soil properties\, plant traits\, occurrence and abundan
 ce of plant or animal species. \n \nIn this session\, you will learn the
  basic concepts and techniques of how to apply remote sensing and machine 
 learning for spatial mapping. However\, we will also discuss current chall
 enges of using machine learning in the context of environmental monitoring
 .\n \nRequirements: \nBasic R skills required\, knowledge in GIS or remo
 te sensing is advantageous\n \n \n\nhttps://events.gwdg.de/event/1313/
LOCATION:CIP I (Büsgenweg 4\, Göttingen)
URL:https://events.gwdg.de/event/1313/
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