Binary Semantic Segmentation: Cloud detection with U-net and Activeloop Hub

The text discusses the challenges in working with remote sensing images due to cloud cover and introduces semantic segmentation as a solution for detecting clouds. It explains that semantic segmentation is a pixel-wise classification technique used in computer vision tasks, such as object detection and image segmentation. The article then presents a tutorial on how to use the U-Net model for binary semantic segmentation of clouds using the 38-Cloud dataset available on Kaggle. It covers data preparation, loading the dataset into TensorFlow format, splitting the dataset into training, validation, and test sets, setting up hyperparameters and metrics, training the model, evaluating its performance, and visualizing the results. The final evaluation shows that the trained model can detect clouds fairly well, although some challenging images may lead to false positives or negatives.

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