UNDERCOVEREISAGENTEN - INTEGRATING LOW-COST UAVS AND COMMUNITY INSIGHTS FOR ENHANCED PERMAFROST MONITORING

This study investigates the integration of low-cost unoccupied aerial vehicles (UAVs) and community engagement in permafrost monitoring. Utilizing novel UAV flight patterns and crowdsourced data analysis, including a Convolutional Neural Network (CNN), the study enhances digital surface models (DSMs...

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Bibliographic Details
Published in:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Main Authors: Mueller, Marlin M., Dietenberger, Steffen, Nestler, Maximilian, Dubois, Clemence, Kaiser, Soraya, Lenz, Josefine, Langer, Moritz, Fritz, Oliver, Marx, Sabrina, Thiel, Christian
Format: Conference Object
Language:unknown
Published: IEEE 2024
Subjects:
Institut für Datenwissenschaften
Datenanalyse und -intelligenz
Arctic
Ice
permafrost
wedge*
Online Access:https://elib.dlr.de/211013/
https://ieeexplore.ieee.org/document/10642540
Description
Summary:This study investigates the integration of low-cost unoccupied aerial vehicles (UAVs) and community engagement in permafrost monitoring. Utilizing novel UAV flight patterns and crowdsourced data analysis, including a Convolutional Neural Network (CNN), the study enhances digital surface models (DSMs) for identifying ice-wedge polygons. Conducted in rapidly changing Arctic regions, it demonstrates an overall accuracy of 74.56% in feature detection. This approach offers improved resolution in environmental monitoring and suggests potential for broader application and rapid disaster response through community-sourced scientific analysis and consumer UAVs.

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