Nahrstedt, FelixGärtner, PhilippWittmann, JochenWohlgemuth, VolkerKranzlmüller, DieterHöb, Maximilian2023-12-152023-12-152023978-3-88579-736-4https://dl.gi.de/handle/20.500.12116/43340Due to the design of the MutliSpectral Instrument on-board Sentinel-2 Satellite, each spectral band observes the ground surface at different times. Recent studies have examined this temporal band offset, mostly to predict aircraft or ship velocities but also for the movement of waves. However, these methods come with the premise of somewhat consistent backgrounds and clear horizontal movements. This paper presents an approach for detecting motion between blue, green and red satellite bands in changing environments to evaluate wind turbine motion using a convolutional neural network (CNN). With this technique, an automatic recognition of turbine activity has been developed, avoiding the barely visible vertical motion in the labelling stage by focusing on the shadow spin. This has been done by creating open source data that is used for training and validation. By focusing on a binary classification approach between spinning wind turbines and non-turbine images, it has been found that a classification is possible and accurate with this method. In addition, limitations and peculiarities of the data and the band offset are described, including an analysis of the occlusion sensitivity. This detection can be useful for precise remote sensing of activity at a given location and is therefore not only of interest to the wind energy industry, which currently only works with proprietary data for energy efficiency or other activity based turbine improvements, but also for environmental monitoring and protection.enRemote Sensing; Convolutional Neural Networks; Wind Turbines; Motion DetectionText/Conference Paper10.18420/env2023-0021617-5468