This is a fancy way of saying we’re going to use some cool computer vision techniques to identify buildings in satellite images.

First, let me explain what YOLT2 is. It stands for You Only Look Twice (version 2), which sounds like something out of a spy movie or maybe a cheesy romance novel. But in reality, it’s just an object detection algorithm that can identify multiple objects at once with high accuracy. In our case, we want to use YOLT2 to find buildings in satellite images.

Now let me explain what SpaceNet data is. It’s basically a massive collection of satellite imagery and associated labels (i.e., information about what’s in the image). This data was created by Maxar Technologies, which is a company that provides geospatial intelligence solutions to businesses and governments around the world.

So how do we use YOLT2 with SpaceNet data? Well, first we need to train our model using some sample images from SpaceNet. We’ll feed these images into YOLT2 along with their associated labels (i.e., information about what’s in each image), and the algorithm will learn how to identify buildings on its own.

Once our model is trained, we can use it to detect buildings in new satellite images that haven’t been labeled before. This process involves feeding these images into YOLT2, which will output a list of bounding boxes (i.e., rectangles) around any building-like objects it finds. We can then use this information to extract features from each building and analyze them in more detail.

For example, let’s say we want to identify the number of stories in each building. To do this, we could count the number of rooflines visible within each bounding box. If there are three distinct rooflines, then we can assume that the building has three stories (assuming a typical single-story house would have one roofline).

Another example might involve identifying the type of material used to construct each building. To do this, we could analyze the spectral signature of each pixel within each bounding box and compare it to known signatures for different materials like concrete or steel. If the spectral signature matches a particular material, then we can assume that the building is made from that material (assuming there are no other factors that might affect the signal).

Overall, this process of using YOLT2 with SpaceNet data has many potential applications in fields like urban planning and disaster response. By identifying buildings in satellite images, we can create detailed maps of cities and towns around the world, which can be used to plan new infrastructure projects or respond more effectively to natural disasters.

Building extraction using YOLT2 and SpaceNet data a fancy way of saying we’re using computer vision techniques to identify buildings in satellite images. It might sound like something out of a spy movie, but trust me, it’s much more exciting than that!