This project aim to use machine learning-intensive Data Analysis Workflows (DAWs) for assessing forest disturbances via remote sensing imagery, aiming to enhance their energy and cost efficiency through optimized update intervals and training processes while harnessing high-volume data from sources like Sentinel satellites, and tackling interdisciplinary challenges in computer science and geoscience.