What do we know about satellite imagery? The most basic definition of satellite imagery is that it is a mechanism by which we receive photos of the Earth taken from space with the help of certain technology. These satellites are like eyes in the sky. With the advent of artificial intelligence (AI) and machine learning (ML), satellite imagery has evolved into something far more exciting and valuable.
Scientists receive a lot of data from these satellite images daily. When this data is tested properly and evaluated, it can provide valuable insight into strategic initiatives, problem-solving, and disaster relief efforts.
There is an ever-increasing number of satellites orbiting the planet Earth. They take millions of photographs from space every day and send them back to the scientists on Earth. Satellite imaging allows for unprecedented distant surveillance of our environment while also generating crucial data on existing conditions on the surface. They also help scientists detect changes in the Earth’s climate, any kind of resource scarcity, the extent of urbanization, etc. Satellite imagery is also used to measure agrarian and industrial growth, human expansion in certain areas and other worldwide developments.
In terms of technology, the world is moving at a breakneck speed. The amount of data sent from satellites for processing and analysis has increased significantly. The number of climatological photos available for human analysis is increasing at an exponential rate. Now the problem arises when the number of climate researchers and meteorological data analysis professionals cannot be increased at the same rate. Under such circumstances, automation is required. Herein comes the uses of AI and machine learning.
Satellite imagery AI applications have been broadened to numerous levels. This is primarily for machine learning research to extract critical information for AI model development in the same fields. There is a need to recognize diverse things from high altitudes for field surveys or urban development. In these conditions, AI models produced through machine learning or deep learning are required for usage in space surveillance via satellite photos.
Satellite imaging, in combination with aerial images and AI, could be used to observe and measure events like topmost tree thinning, loss of undergrowth native vegetation, etc. Applications of AI through machine learning can also be used to measure alterations in water levels. Furthermore, the development of new vegetation in areas that are being revived can also be tracked by artificial intelligence technology.
Artificial Intelligence allows for the analysis and correlation of a vast number of climate variables. The use of both terrestrial and geographical data is critical for establishing connections between all these parameters. This is to gain a better awareness of global challenges and offer solutions. It gives crucial aspects for interpreting the trajectory of the environment and its influence on our existence to all policy-makers (NGOs, governments, and regular citizens).
Satellite imagery technology has advanced significantly in recent years, mainly due to advances in machine and deep learning. Machine learning approaches enable engineers to enhance picture data in addition to image editing. Machine Learning algorithms have shown to be an effective technique for interpreting satellite data at any resolution and providing more nuanced insights. There are a few hurdles in applying Machine Learning to satellite photos in its early phases, along with the extremely huge file size of satellite imagery and the file formats being specifically built for geo-referenced photographs, which makes Big Traditional data Processing applications challenging. However, analyzing the concept of the time value of a solution and building a powerful framework facilitates the construction of programmes to solve these restrictions.
Machine Learning is also used heavily for satellite pictures by Earth Observation companies like DigitalGlobe and Planet. Planet Analytics is a specialized Machine Learning system that employs Machine Learning algorithms to scan regular satellite images, recognize and classify structures, locate topographical and geographic characteristics, and reliably observe even just the smallest changes that occur. The data feed is readily incorporated into procedures and provides remarkable insights into practically any location on the planet.
There are two groups when it comes to the application of ML models in satellite imagery. They are:
One-level Satellite Data Applications
Multi-level Satellite Data Applications
We will discuss them one by one along with examples of each category.
Machine learning algorithms are used directly in satellite imagery in "one-level" applications. This type of satellite data is used to observe small objects and detect changes through the use of AI.
For example, change detection algorithms heavily depend on machine learning in satellite imagery. Agriculture, farmland use, public development (e.g. road segments), environmental (deforestation, water reserves), and global disaster monitoring are all examples of change detection use cases. Change detection derived from remote sensing (RS) data is a widely used way of identifying alterations on the Earth's surface.
Change Detection Algorithms
The practice of finding differences in the condition of an object or phenomenon by watching it at different times is known as change detection. Satellite images are used to detect digital changes. In simple words, a change detection algorithm is the ability of a system to correctly perceive external input, learn from it, and apply what it has learned to fulfil specific goals and tasks. This is achieved through flexible adaptation. Several AI techniques are inspired by biological phenomena. They mainly concentrate on the advent of modern deep learning methods, innovative network topologies, and sophisticated machine learning methods.
Computer Vision Techniques
The fundamental use of satellite data is to enable computer vision to detect diverse objects. Computer vision is a branch of artificial intelligence (AI) that allows machines and programs to extract useful data from virtual photos, videos, and other sensory inputs. Computer vision can also participate in executing certain measures to make predictions based on that data.
Municipalities, government bodies, emergency crews, the armed forces, and other civil authorities require reliable information on structures, route sections, and urban area boundaries. A variety of integrated, fully prepared workflows using convolutional neural nets (eg. FasterRCNN, YOLO) have made object detection easier. However, there are several difficulties with satellite images that make this job more difficult. The potential for adapting these technologies to various scales and important assets in satellite photography is enormous.
Information retrieved from satellite data is only a sequence of parameters in a more complicated ML system in multi-level applications. It can range anywhere from recognizing cars in parking lots to predicting crop yields, detecting oil inventories, etc. With the help of multi-level satellite data applications, scientists can also track the economic activities of several countries throughout the world. It is especially applicable for those areas that are difficult to reach, such as China.
As an example of a multi-level satellite data application, we can take into account agricultural field mapping from the air. Data from satellite photos can aid the AI model in predicting crop productivity and prices. The AI model, which is based on satellite imagery, has shown to be a very effective tool for yield estimation.
The ability of scientific and commercial communities to evaluate satellite images is still far outpacing its accessibility. While AI has made tremendous progress in satellite imaging, there is still a long way to go. Satellite imaging data obtained due to machine learning is an invaluable resource for addressing several problems. These problems can be as diverse and essential as assessing the effects of global warming, forecasting agriculture yields, etc.
The impact of artificial intelligence in satellite imaging can also be seen in measuring environmental development toward the Sustainable Development Goals (SDGs). AI, particularly the fast-evolving sub-disciplines like machine and deep learning, offers the key to rendering satellite photo processing smoother, more robust, and more universally accepted.