In this article, I’ll be illustrating how to approach a core computer vision problem known as semantic segmentation. Simply put, semantic segmentation’s goal is to simply classify each pixel in a given image to a particular class according to what is shown in the image.

LNDST is a classic example of semantic segmentation which can be solved using CNNs. The Landsat dataset consists of 400x400 RGB satellite images that have been taken from the Landsat 8 satellite. In each image, there can be water and background. Our classifier should predict each pixel as 0 - background or 1 - water. The metric for ranking is the F1/dice score.

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