Use examples when they help make things clearer.

One important application is remote sensing image classification, which involves using computer algorithms to identify different types of land cover (such as forests, farms, or urban areas) based on patterns in satellite images.

To do this, researchers typically use a technique called deep learning, which involves training neural networks to recognize specific features and patterns within the data. This can be done through a process known as supervised learning, where labeled examples are used to teach the network how to classify new, unlabeled images based on similarities in their underlying structure.

For example, let’s say we have a dataset of satellite images that includes labels for different types of land cover (such as “forest” or “urban”). We can use this data to train a neural network using a technique called convolutional neural networks (CNNs), which are specifically designed for image classification tasks.

During the training process, the CNN is fed a large number of labeled images and learns how to identify specific features that distinguish one type of land cover from another. For example, it might look for patterns in the color or texture of the pixels within an image to determine whether it represents a forest or an urban area.

Once the network has been trained, we can use it to classify new, unlabeled images based on similarities in their underlying structure. This can be done using a technique called transfer learning, which involves fine-tuning a pretrained CNN (such as ResNet) for our specific classification task.

For example, let’s say we have a dataset of satellite images that includes labels for different types of land cover, but we don’t have enough labeled data to train a new CNN from scratch. Instead, we can use transfer learning to fine-tune an existing pretrained model (such as ResNet) on our specific classification task using only a small amount of labeled data.

This approach has been shown to be highly effective for remote sensing image classification tasks, and is currently being used by researchers around the world to monitor everything from deforestation rates in the Amazon rainforest to urbanization patterns in major cities like New York or Los Angeles.