Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI
Nina Merkle Reza Bahmanyar Corentin Henry Seyed Majid Azimi Xiangtian Yuan Simon Schopferer Veronika Gstaiger Stefan Auer Anne Schneibel Marc Wieland Thomas Kraft
In order to respond effectively in the aftermath of a disaster, emergency
services and relief organizations rely on timely and accurate information about
the affected areas. Remote sensing has the potential to significantly reduce
the time and effort required to collect such information by enabling a rapid
survey of large areas. To achieve this, the main challenge is the automatic
extraction of relevant information from remotely sensed data. In this work, we
show how the combination of drone-based data with deep learning methods enables
automated and large-scale situation assessment. In addition, we demonstrate the
integration of onboard image processing techniques for the deployment of
autonomous drone-based aid delivery. The results show the feasibility of a
rapid and large-scale image analysis in the field, and that onboard image
processing can increase the safety of drone-based aid deliveries.