Atrous convolutions, also known as dilated convolutions, enhance convolutional neural networks by expanding the receptive field without increasing parameters, making them ideal for high-resolution image analysis. Introduced in 2016, they have since become a cornerstone in tasks like semantic segmentation, object detection, and geospatial analysis.
