Advancing Environmental Applications through Machine Learning and Computer Vision: Modeling, Algorithms, and Real-World Implementations

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My research focuses on advancing environmental applications through the development and implementation of vision-based algorithms. By harnessing the power of computer vision and machine learning, I can accurately estimate high spatial resolution air pollutant concentrations and address broader environmental challenges. This involves leveraging commodity consumer cameras, analyzing light attenuation, and creating portable and cost-effective image-based sensing methods for urban and industrial areas. The research also explores fire detection systems and remote sensing segmentation tasks using novel principles.

   Key contributions include highlighting the need for high spatial and temporal resolutions in air pollution exposure estimation, developing a multi-pollutant estimation technique, creating a publicly available dataset for evaluation, analyzing the impact of sensor density and camera presence, proposing novel approaches for nighttime PM2.5 estimation and image-based PM2.5 forecasting, introducing a Context-Oriented Multi-Scale Network for fire segmentation, and enhancing feature representation in remote sensing segmentation.

   By accurately estimating air pollutant concentrations, detecting fires, and performing remote sensing segmentation, decision-makers can monitor, manage, and respond to environmental challenges more effectively. The development and implementation of these algorithms contribute to a world where machine learning and computer vision play a crucial role in safeguarding public health and the environment.

Chair: Professor Robert Dick