Semantic Real-Time Mapping with UAVs

Abstract Whilst mapping with UAVs has become an established tool for geodata acquisition in many domains, certain time-critical applications, such as crisis and disaster response, demand fast processing pipelines rather than photogrammetric post-processing approaches. Based on our 3D-capable real-time pipeline, this contribution presents not only array of optimisations the original implementation but also extension towards understanding image content respect to land cover object detection using machine learning. This paper (1) describes pipeline its entirety, (2) compares performance semantic labelling models quantitatively (3) showcases real-world experiments qualitative evaluations.