L
Linda See
Researcher at International Institute for Applied Systems Analysis
Publications - 338
Citations - 13633
Linda See is an academic researcher from International Institute for Applied Systems Analysis. The author has contributed to research in topics: Land cover & Crowdsourcing. The author has an hindex of 55, co-authored 312 publications receiving 10755 citations. Previous affiliations of Linda See include International Institute of Minnesota & University College London.
Papers
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Journal ArticleDOI
A comparison of global agricultural monitoring systems and current gaps
Steffen Fritz,Linda See,Juan Carlos Laso Bayas,François Waldner,François Waldner,Damien Christophe Jacques,Inbal Becker-Reshef,A. K. Whitcraft,Bettina Baruth,Rogerio Bonifacio,Jim Crutchfield,Felix Rembold,Oscar Rojas,Anne Schucknecht,Marijn van der Velde,James P. Verdin,Bingfang Wu,Nana Yan,Liangzhi You,Sven Gilliams,Sander Mücher,Robert Tetrault,Inian Moorthy,Ian McCallum +23 more
TL;DR: This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate.
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Comparing the quality of crowdsourced data contributed by expert and non-experts.
Linda See,Alexis Comber,Carl F. Salk,Carl F. Salk,Steffen Fritz,Marijn van der Velde,Christoph Perger,Christoph Perger,Christian Schill,Ian McCallum,Florian Kraxner,Michael Obersteiner +11 more
TL;DR: Examination of crowdsourced data from the Geo-Wiki crowdsourcing tool for land cover validation showed that there was little difference between experts and non-experts in identifying human impact although results varied by land cover while experts were better than non- experts in identifying the land cover type.
Journal ArticleDOI
Mapping citizen science contributions to the UN sustainable development goals
Dilek Fraisl,Dilek Fraisl,Jillian Campbell,Linda See,Uta Wehn,Jessica Wardlaw,Margaret Gold,Inian Moorthy,Rosa Arias,Jaume Piera,Jessica L. Oliver,Jessica L. Oliver,Joan Masó,Marianne Penker,Steffen Fritz +14 more
TL;DR: In this paper, a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, is presented.
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Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications
TL;DR: This paper provides a methodology for comparing global land cover maps that allows for differences in legend definitions between products to be taken into account, whereby the user rates the importance of disagreement between different legend classes based on the needs of the application.
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Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia
TL;DR: The results showed that the genetic algorithm produces a better calibrated model than parallel simulated annealing and the model that contains all primary drivers and all interactions produced the best performing calibrated model overall.