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 geodata processing pipelines rather than photogrammetric post-processing approaches. Based on our 3D-capable real-time mapping pipeline, this contribution presents not only an array of optimisations of the original implementation but also an extension towards understanding the image content with respect to land cover and object detection using machine learning. This paper (1) describes the pipeline in its entirety, (2) compares the performance of the semantic labelling and object detection models quantitatively and (3) showcases real-world experiments with qualitative evaluations.