MEASUREMENT OF INDUSTRIAL SMOKE PLUMES FROM SATELLITE IMAGES

Reducing industrial greenhouse gas (GHG) emissions has become imperative for mitigating the adverse effects of climate change. Accurate measurement and monitoring of industrial smoke plumes, which are a significant source of GHG emissions, are crucial for effective emission control strategies. This paper addresses the prospect of utilizing satellite images to measure industrial smoke plumes and explores the effectiveness of various computer vision (CV) technologies in this context. The study focuses on examining both modern deep learning and traditional machine learning models for detecting and segmenting industrial smoke plumes in satellite images. While deep learning models have shown remarkable performance in various CV tasks, their ability to accurately segment smoke plumes in satellite images remains limited, with an average intersection over union (IOU) of no more than 60%. However, certain deep learning models, such as U-Net and AttU-Net, exhibit promising capabilities in identifying challenging types of noise, including clouds, white building surfaces, and snow, which traditional machine learning models struggle with. Employing deep learning models for industrial smoke plume detection proves advantageous, as all models achieve an approximate detection accuracy and F1-Score of 90%. The findings from this research serve as a valuable foundation for further advancements in developing advanced deep learning models specifically tailored to handle the identified types of noise.