machine learning to identify clouds on NASA satellite images
Organization
Scaling and Intelligence Lab (SaIL)
Abstract
Clouds polluted by anthropogenic aerosols, i.e. microscopic solid and liquid air pollution
particles, at anthropogenic aerosol point sources (Fig 1) help to better understand
anthropogenic impacts on Earth’s climate (Toll et al., 2019). However, visual/manual
identification of polluted cloud tracks is time-consuming. Machine learning has been used to
identify clouds polluted by shipping emissions (Yuan et al., 2019; Yuan et al., 2022;
Watson-Parris et al., 2022)
particles, at anthropogenic aerosol point sources (Fig 1) help to better understand
anthropogenic impacts on Earth’s climate (Toll et al., 2019). However, visual/manual
identification of polluted cloud tracks is time-consuming. Machine learning has been used to
identify clouds polluted by shipping emissions (Yuan et al., 2019; Yuan et al., 2022;
Watson-Parris et al., 2022)
Graduation Theses defence year
2023-2024
Supervisor
KALLOL ROY, Velle Toll, Piia Post
Spoken language (s)
Estonian, English
Requirements for candidates
Level
Bachelor, Masters
Application of contact
Name
KALLOL ROY
Phone
+37256051480
E-mail
Full Document
See more