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.

翻译：为了在灾后有效响应，应急服务与救援组织依赖于受灾区域及时且准确的信息。遥感技术能够快速勘测大面积区域，从而显著减少收集此类信息所需的时间与人力。实现这一目标的主要挑战在于从遥感数据中自动提取相关信息。本文展示了如何将基于无人机的数据与深度学习方法相结合，实现自动化、大规模的情势评估。此外，我们演示了机载图像处理技术的集成，用于部署自主化无人机辅助物资投递。研究结果表明，在现场进行快速大规模图像分析具有可行性，且机载图像处理能够提升无人机物资投递的安全性。