Use examples when they help make things clearer.

Let me refine the original answer for you based on your new context:

Satellite image segmentation using deep learning techniques involves feeding a neural network all the pixels in an image, telling it what to look for (like buildings or trees), and then letting it figure out which pixels belong together based on their similarities. This is done by splitting the image into smaller chunks called patches that are fed through the neural network one at a time. Each patch is labeled as either “building” or “not building”, based on some predefined criteria like color or texture. The neural network uses this labeling information to learn which features are most important for distinguishing between buildings and non-buildings, and then applies that knowledge to segment the entire image into its component parts (like a jigsaw puzzle).

For example, let’s say you have this satellite image of a city:

[Insert Image]

To use deep learning for segmentation, we first preprocess the image by converting it to grayscale and resizing it to 256×256 pixels. Then, we split the image into patches (like puzzle pieces) that are fed through a neural network one at a time:

[Insert Image of Patches]

Each patch is labeled as either “building” or “not building”, based on some predefined criteria like color or texture. The neural network uses this labeling information to learn which features are most important for distinguishing between buildings and non-buildings, and then applies that knowledge to segment the entire image into its component parts:

[Insert Image of Segmented City]

The result is a map of the city showing where each building is located. Pretty cool, huh?

In terms of recent research in this area, there have been several notable developments. For example, researchers at Google recently introduced a new method called “DeepHighRes” for learning high-resolution representations using transformers. This approach has shown promising results on semantic segmentation tasks and is particularly effective when combined with other techniques like object region mining (ORAM) or adversarial erasing.

Another recent development in this field involves the use of unsupervised semantic segmentation adaptation for improving model performance without requiring additional labeled data. This approach has shown promising results on a variety of tasks, including image classification and object detection, and is particularly useful when working with limited or noisy training data.