Use examples when they help make things clearer.

Let’s talk about deep learning for land use classification using Sentinel-2 imagery. This is a fancy way of saying we’re gonna teach computers to look at satellite images and figure out what kind of stuff is on the ground below.

First, let me explain how this works in simpler terms: imagine you have a bunch of pictures (like from Google Maps) and you want your computer to tell you if there are trees or buildings in them. You can do this by showing it lots of examples with labels (like “tree” or “building”) so that it learns what each one looks like. Then, when you give it a new picture, it can use its fancy algorithms to figure out which label fits best based on the features it sees.

Now Let’s get started with some technical details: we’re using Sentinel-2 imagery because it has high spatial resolution (meaning we can see small things like trees and buildings) and covers a large area of land. We’re also using deep learning techniques, specifically convolutional neural networks (CNNs), which are really good at recognizing patterns in images.

Here’s an example of how this might work: let’s say we have a picture with some trees and buildings in it. The computer looks at the image and identifies different features like edges, corners, and textures (like the bark on a tree or the bricks on a building). It then uses these features to create a “feature map” that represents each part of the image as a set of numbers.

Next, it passes this feature map through multiple layers of neurons in the CNN, which learns how to combine and interpret different sets of features based on what it’s seen before. This allows it to recognize patterns that might not be immediately obvious to us humans (like the shape of a tree canopy or the layout of a building).

Finally, the computer uses this information to make a prediction about which label fits best for each part of the image. It does this by comparing the feature map to a database of labeled images and choosing the one that matches most closely based on its features. This allows us to classify large areas of land with high accuracy and efficiency!

It’s like magic for your computer!