2021: Synthetic Geospatial Imagery’s Time to Shine

Throughout the last year, the machine learning and artificial intelligence sectors saw considerable growth throughout a multitude of industries. Although their utilization continues to grow, there are still far too many drawbacks and challenges associated with its implementation. 

With the growing utilization of AI models, the increase in the number of available geospatial images, and the affordability in computational power, we at OneView believe that 2021 will be a year of growth for all types of visual-based analytics, in particular, AI models such as geospatial computer vision.

What Is Geospatial Computer Vision?

computer vision is a way for an AI model to analyze visual data to locate and identify specific objects. Geospatial computer vision, most often associated with satellite and aerial images, is a way of enabling an AI model to ‘view’ geospatial imagery to more quickly and accurately analyze visual data.

The Geospatial Computer Vision Process

In order to properly train a computer vision AI model to locate and identify specific objects, it’s necessary to present it with thousands of images so that you can ‘teach’ it what to look for. A generic geospatial computer vision training process would look like this:

Step 1) Find as many geospatial images as possible that are associated with your model’s intended result, such as object detection or segmentation

Step 2) Annotate the images manually so that the AI model will be able to properly recognize what it should be ‘looking’ for

Step 3) Curate the images to ensure that there are sufficient images that include variations in variables such as viewing angles, lighting conditions, climate, and object appearances 

Step 4) Train the algorithm based on your desired AI architecture

Step 5) Test the model to assess its accuracy

Step 6) Repeat steps 1-4 until the model has a sufficiently high accuracy rate for object detection for your desired outcome

Road Blocks to Increasing Computer Vision Use Cases

The most significant barrier for computer vision is quite simply the huge amount of time and images needed to properly train an algorithm. In order to obtain access to the critical mass of geospatial imagery needed could take months of collecting. In addition to that, once you have the critical mass of geospatial imagery, a human still needs to go through each and every image to identify and annotate the desired object for identification.

Since the computer vision AI training process requires a human being to identify and annotate objects for the computer, training times can be prolonged, possibly indefinitely. Humans aren’t perfect, and can easily make mistakes. This is especially true with regards to geospatial images, where the resolutions are fairly low, and distinguishing between different objects could be incredibly challenging. Missing an object or misidentifying an object will reduce the model’s ability to recognize it, and could cause the model to stop functioning entirely.

Finally, and most significantly, for new and emerging objects that have rarely been seen, such as unique military equipment, oil field leaks, and even powerline vegetation management, there may not be sufficient geospatial imagery available to train a computer vision AI model at all.

Synthetic Images: Geospatial Computer Vision’s Silver Bullet

Synthetic geospatial images are a way to create a limitless number of use cases by reducing the amount of real-world geospatial images needed to train a computer vision model. Not only does synthetic geospatial data make the training process easier, but it also significantly reduces the amount of time needed to train a model, from months to weeks.

What Will Computer vision Models Look Like in 2021 and Beyond?

As the demand for better and more reliable computer vision models continues to rise, so will new use cases, but it won’t be possible without the help of synthetic geospatial images.

Synthetic geospatial images enable the creation of computer vision object detection models regardless of how many real-world images exist. Furthermore, you will also be able to place your object in various environments, climates, and lighting, which will further increase the effectiveness and accuracy of your model, on a global scale, and in far less time than had you been using real-world images alone.

It is for these reasons that we believe that 2021 will see an explosion in the utilization of synthetic geospatial images specifically for the purpose of computer vision training. We believe that this trend will extend across virtually every sector and industry. 

Don’t get left in the dark, start learning about how synthetic geospatial imagery can supercharge your computer vision AI model today!