| Summary: | As global warming causes climate change, extreme weather has become more common, posing a significant threat to life on Earth. One of the important indicators of climate change is the formation of melt ponds in the arctic region. Scarcity of large amount of annotated arctic sea ice data is a major challenge in training a deep learning model for the prediction of the dynamics of the melt ponds. In this research work, we use diffusion model, a class of generative models, to generate synthetic arctic sea ice data for further analysis of meltponds. Based on the training data, diffusion models can generate new and realistic data that are not present in the original dataset by focusing on the data distribution from a simple to a more complex distribution. First, simple distribution is transformed into a complex distribution by adding noise, such as a Gaussian distribution and through a series of invertible operations. Once trained, the model can generate new samples by starting from a simple distribution and diffusing it to the complex distribution, capturing the underlying features of the data. During inference, when generating new samples, the conditioning information is provided as input alongside the starting noise vector. This guides the diffusion process to produce samples that adhere to the specified conditions. We used high-resolution aerial photographs of Arctic region obtained during the Healy-Oden Trans Arctic Expedition (HOTRAX) in year 2005 and NASA’s Operation IceBridge DMS L1B Geolocated and Orthorectified data acquired in 2016 for the initial training of the generative model. The original image and synthetic image are assessed based on their chromatic similarity. We employed evaluation metric known as Chromatic Similarity Index (CSI) for the assessment purposes.
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