Aser Cortines

Abstract Cloud and cloud shadow masking is a crucial preprocessing step in hyperspectral satellite imaging, enabling the extraction of high-quality, analysis-ready data. This study investigates several lightweight machine learning models that require fewer resources and can be more easily deployed on satellites. We evaluate various machine learning approaches, including gradient boosting methods such as XGBoost and LightGBM as well as convolutional neural networks (CNNs).

Today’s energy industry is undergoing profound changes in response to the demands of climate change mitigation. To improve their operations, optimize resources, and enhance customer experiences, utilities are increasingly turning to data-driven solutions and adopting cutting-edge methods to tackle the increasing complexity posed by the green transition.