Essentially, what we’re talking about is using a computer to look at images of the environment (like forests or cities) and figure out which parts are trees, buildings, roads, etc.

Now, how does it work? Well, first you feed these images into a fancy algorithm called a neural network. This neural network has lots of layers that can learn to recognize different patterns in the data. For example, one layer might be able to identify edges and corners (which are often found around building structures), while another layer might look for green pixels (which could indicate trees or grass).

Once these layers have learned how to identify different features, they pass their results on to a final “output” layer that puts everything together. This output layer can then tell us which parts of the image are most likely to be buildings, roads, etc.

Here’s an example: let’s say we feed in this image of a city street:

The neural network might identify the following features:
– Edges and corners around building structures (red boxes):

! [image](https://i.imgur.com/7V5a8sO.png)

– Green pixels in grassy areas (green box):

! [image](https://i.imgur.com/qZJcKj.png)

– Road markings and lines (blue boxes):

! [image](https://i.imgur.com/XZJzcKj_1.png)

Once the neural network has identified all of these features, it can pass them on to an output layer that tells us which parts of the image are most likely to be buildings, roads, etc. In this case, we might get something like:

– Buildings (red):

! [image](https://i.imgur.com/XZJzcKj_2.png)

– Grass and trees (green):

! [image](https://i.imgur.com/qZJcKj_1.png)

– Roads (blue):

! [image](https://i.imgur.com/XZJzcKj_3.png)

And that’s it! By using a neural network to identify different features in the environment, we can create detailed maps and models of our surroundings without having to manually label every single pixel. Pretty cool, huh?