Artificial Intelligence Applications in Satellite Imagery: Enhancing Data Usage
AI applications in satellite imagery are used to train machine learning and make effective AI models.
Historically, space has been an industry that houses government and heavyweight airspace satellites. But the recent emergence of Artificial Intelligence (AI) and its features has opened the door for small players to intervene the space exploration. Even though AI in space is technically expensive, it has made the industry more accessible. Particularly, AI applications in satellite imagery are used for various purposes like training machine learning and making effective AI models out of it.
Since the birth of commercial satellites in the 1960s, space exploration has grown to deliver a broad range of essential services to people around the globe. These commercial satellites played a key role in delivering television broadcasting and providing emergency telecommunications, global positioning, meteorological information, and environmental monitoring. Satellite images allow us to view the earth from every aspect. Artificial intelligence applications in space imagery are also helping in enhancing sustainable development. Satellite images are used to train Artificial intelligence models that use machine learning and deep learning to detect various objects in space. The satellite image data includes different types of aerial views such as agriculture fields, forests, metropolitan cities, rural areas, agricultural lands, etc. AI can transform how data is processed both in space and on earth, increasing the speed at which the insights can be delivered. Henceforth, GlobalTech Outlook brings you a list of Artificial Intelligence applications in satellite imagery that enhances the space industry.
‘One-step’ satellite data applications
Object detection plays a pivotal role in the space industry. The information on buildings, road segments, and urban area boundaries is important for municipalities, government agencies, rescue teams, military, and other civil agencies. However, there are some issues with satellite imagery, even though the object detection task has been enabled by several integrated ready-to-use pipelines using convolutional neural nets. Researchers are working on making computer vision detect the exact objects. The big problem with object recognition is that the images are microscopic as it is captured from space. Fortunately, space and tech industries are working on the issue to be corrected in the future.
‘Multi-level’ satellite data applications
Multi-level applications are the ones in which information extracted from satellite imagery is only a line of features in a more complex machine learning system. Since the object detection task has been allowed by several integrated ready-to-use pipelines with convolutional neural nets, artificial intelligence is having a hard time naming it properly. To finalize the pipeline-based application, Artificial Intelligence must perform tasks like finding the required imagery, extracting the needed information, and name it properly.
Elevating clarity and visualization
Images of earth and other substance in the universe are taken from a far distance. The long-distance views usually break the clarity of the image. In order to utilize the image properly, AI is being deployed to elevate the clarity and visualization. For example, Airbus uses Artificial Intelligence for the past few years to improve the quality of satellite imagery it delivers to its customers. AI is being employed in stacks of temporal images to monitor Earth and make AI analysis of imagery onboard the satellite so that image requests can be automatically reprogrammed, gaining precious time to generate useful insights.
Creating ground-level imagery
Besides improving the image clarity and enhancing visualization, machine learning interprets the satellite images in many ways and from different vantage points. For example, scientist Xueqing Deng and his colleagues at the University of California created a machine learning algorithm that builds ground-level images by looking at aerial satellite imagery. They used a machine intelligence technique called a generative adversarial network that consists of two neural networks: generator and discriminator.