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) 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.