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; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 3770-3774

Abstract

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.

Related Material

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[bibtex]
@InProceedings{Merkle_2023_ICCV, author = {Merkle, Nina and Bahmanyar, Reza and Henry, Corentin and Azimi, Seyed Majid and Yuan, Xiangtian and Schopferer, Simon and Gstaiger, Veronika and Auer, Stefan and Schneibel, Anne and Wieland, Marc and Kraft, Thomas}, title = {Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2023}, pages = {3770-3774} }