Today, I want to tell you the reason why it is difficult to use AI for analyzing high-resolution images.
I found many earth scientists use conventional AI models which process normal sized images. However, these attempts usually do not yield good results.
First of all, let me explain why this is happening.
Common AI models, for example the object detection model, are designed for the photos we take every day. So, if you put in high-resolution images such as satellite images or aerial images, common libraries will probably raise errors.
Or it will shrink your high resolution image like a normal image to process it in the AI model. And, a lot of objects contained in your high-resolution image will become invisible as they get smaller. Then, of course, the AI model will not be able to be trained and predict properly.
Some earth scientists who have come to realize this will make new attempts.
Some of them will say this.
“Can't I just make the model bigger?”
However, a lot of problems arise at this moment.
First, you need to implement an AI model that processes large-scale images, and from here on, you must have knowledge at the level of an AI researcher. Earth scientists who have been looking for a deep learning library will be frustrated when they realize they need a master's or doctoral level of knowledge in computer science and computer vision.
And the bigger problem is that your GPU already uses a lot of VRAM to process normal size photos. So the moment you increase the size of the model, your GPU will scream "Out of memory".
Even if you found an expensive GPU with large VRAM capacity, you cannot solve this problem. Because high-resolution images such as aerial photography are difficult to process even if you increase your VRAM by 4 times. And such graphics cards do not exist on the market.
A few remaining people will think about what to do with this image and come to the conclusion that the image needs to be segmented.
You approached the correct answer more.
However, you will experience another frustration here. You may slice an image with a python code, but it takes too much time to cut a huge satellite or aerial images.
Here you will be puzzled what to do.
What you need at this time is the help of a computer scientist.
Computer scientists do not study geospatial information, but since undergraduate students, they continue to learn how to process large data quickly, with limited computer resources.
With the maximum use of CPU cores and optimal algorithms applied, computer scientists will solve these problems.
Unless you're a computer science major, it's hard to use the skills like cluster computing, parallel processing, and Heterogeneous Computing.
Therefore, leave it to experts like us! We would love to help you. ☺
We implemented Deep Block by applying multiprocessing, cluster computing, and virtualization technologies, so Deep Block can process high-resolution images.
We can also provide custom solutions and consulting for you. Please feel free to contact us if you need assistance.