Adaptive sampling using a neural network and a fuzzy regulator is described as applied to computer network traffics. The objective of this approach is to maximally reduce the amount of data to be processed with preservation of acceptable measurement accuracy. The results of experimental verification of sampling efficiency are also presented that are based on the traffic data archive of a real computer network.

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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.