Semantic segmentation involves labeling each pixel in an image with a class. One application of semantic segmentation is tracking deforestation, which is the change in forest cover over time. In this project, RIT-18 dataset was used in order to perform Semantic Segmentation for Multispectral images (check out https://github.com/rmkemker/RIT-18 to find out more). Main analyses are based on U-Net architecture, fully convolutional networks, whose architectures were modified in order to yield more precise segmentation.
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Semantic segmentation in order to track deforestation for the RIT-18 dataset. Main analyses are based on U-Net architecture, fully convolutional networks, whose architectures were modified in order to yield more precise segmentation.
juliabuixuan/Deep-Learning
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Semantic segmentation in order to track deforestation for the RIT-18 dataset. Main analyses are based on U-Net architecture, fully convolutional networks, whose architectures were modified in order to yield more precise segmentation.
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