Wetlands scientists require detailed maps of the land and water contours to correctly understand wetlands water systems. Current methods rely on expensive, manually collected data that are rarely updated, providing obsolete data for modeling. Automating the process would improve data collection and enable scientists to improve wetlands models. The automated solution needs to minimally interact with the fragile environment, to function autonomously to avoid long trips to remote wetland locations, and to provide modularity to support the different measurement techniques for topographic and bathymetric mapping. Unmanned aerial vehicles with varying payloads and autonomous behavior provide a reasonable and effective solution.


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    Titel :

    UAV-Based Automated Labeling of Training Data for Online Water and Land Differentiation


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Xiao, Jing (Herausgeber:in) / Kröger, Torsten (Herausgeber:in) / Khatib, Oussama (Herausgeber:in) / Klein, Curtis (Autor:in) / Speckman, Trevor (Autor:in) / Medeiros, Thomas (Autor:in) / Eells, Derek (Autor:in) / Basha, Elizabeth (Autor:in)

    Kongress:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Erscheinungsdatum :

    23.01.2020


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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