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Modern before/after image differencing methods increasingly use deep convolutional neural networks (such as U-Net, ResNet, etc.) for semantic segmentation, classifying image pixels into categories such as buildings, roads, water bodies, and vegetation, and then determining whether the state of each category has undergone abnormal changes.
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10/16/2026
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