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In order to respond effectively in the aftermath of a disaster, emergencyservices and relief organizations rely on timely and accurate information aboutthe affected areas. Remote sensing has the potential to significantly reducethe time and effort required to collect such information by enabling a rapidsurvey of large areas. To achieve this, the main challenge is the automaticextraction of relevant information from remotely sensed data. In this work, weshow how the combination of drone-based data with deep learning methods enablesautomated and large-scale situation assessment. In addition, we demonstrate theintegration of onboard image processing techniques for the deployment ofautonomous drone-based aid delivery. The results show the feasibility of arapid and large-scale image analysis in the field, and that onboard imageprocessing can increase the safety of drone-based aid deliveries.

Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI