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SUMMARY:Remote sensing and machine learning for environmental mapping
DTSTART:20241206T130000Z
DTEND:20241206T170000Z
DTSTAMP:20260815T065700Z
UID:indico-event-950@events.gwdg.de
CONTACT:kmeyer5@uni-goettingen.de
DESCRIPTION:Speakers: Hanna Meyer (University of Münster)\n\nOne key task
  in environmental science is to map environmental variables continuously i
 n space or even in space and time as a baseline to inform decision-making 
 in various applications\, such as agriculture\, land-use planning\, or nat
 ural resource management\; or to study ecological research questions based
  on spatial patterns. However\, most ecological variables are only availab
 le as point data\, e.g. from field surveys. Modelling approaches are hence
  required to move from local field observations to continuous maps of ecol
 ogical variables by estimating the value of the variable of interest in pl
 aces where it has not been measured. \n \nIn recent years\, machine lear
 ning methods have become a popular tool to learn patterns in nonlinear and
  complex systems. They have been applied to map various ecological variabl
 es\, even ambitiously on a global scale\, such as land cover\, soil proper
 ties\, plant traits\, occurrence and abundance 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 challenges of using machine learnin
 g in the context of environmental monitoring.\n \nRequirements: \nBasic 
 R skills required\, knowledge in GIS or remote sensing is advantageous\n 
 \n \n\nhttps://events.gwdg.de/event/950/
LOCATION:CIP I (Büsgenweg 4\, Göttingen)
URL:https://events.gwdg.de/event/950/
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