marcosPlaza/Ground-based-Cloud-Classification-with-Deep-Learning
Implementation of different Deep Learning algorithms to solve the problem of cloud classification, using images taken from the ground.
This project helps meteorologists, climate scientists, and weather observers automatically identify cloud types from ground-based images. You input photographs of clouds and the system outputs a classification of the cloud's physical constitution, such as Cumulus, Cirrus, or Stratus. This can speed up weather prediction and climate characterization by reducing reliance on time-consuming manual observation.
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Use this if you need an automated, accurate way to classify cloud types from ground-level images for meteorological or climate research.
Not ideal if you need cloud classification based on criteria other than physical constitution, such as height or development, or if you are working with satellite imagery.
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Jupyter Notebook
License
MIT
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Last pushed
Jul 12, 2022
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