We want to teach a computer how to identify those solar panels and mark them on the picture so we can see where they are without having to manually go through each one ourselves.

We use something called “deep learning” which is essentially just a fancy way of saying that we teach the computer how to recognize patterns and make decisions based on those patterns. In this case, we want the computer to learn what solar panels look like so it can identify them when it sees them in new pictures.

To do this, we feed our image dataset into something called a “neural network” which is essentially just a bunch of math equations that help us analyze and understand patterns in data. The neural network takes the input images (which are made up of pixels) and breaks them down into smaller pieces called features. These features are then fed through more layers of math equations to create even more complex features until we finally get an output that tells us whether or not a particular image contains solar panels.

Now, how this actually works in practice. First, we take our input images and feed them into the neural network. The neural network then breaks down each image into smaller pieces called features (which are essentially just patterns within the image). These features are fed through more layers of math equations to create even more complex features until we finally get an output that tells us whether or not a particular image contains solar panels.

The output is basically just a number between 0 and 1, where 0 means “no solar panel” and 1 means “solar panel present”. We can then use this output to mark the location of any solar panels in our input images using something called a “mask”. A mask is essentially just a black-and-white image that shows us which parts of the original image contain solar panels (in white) and which parts do not (in black).

There you go! That’s how we use deep learning to detect solar panels in images. It might sound complicated at first, but once you break it down into simpler terms it becomes much easier to understand.