AI in Satellite Data Analysis and Interpretation
Satellite data creates some of the most beneficial information that we use in our modern, digital world. By placing monitors far above Earth, we can monitor a robust number of systems from geography and climate, to more human developments like traffic and urban development.
As our dependency on this valuable information continues to grow, so does our reliance on it and the need to extract further amounts of data to support our growing technological economy. Fortunately, with the advent of artificial intelligence (AI) and its growing use around the world, we are able to create advanced models that can routinely analyze the massive amounts of raw data being captured by satellites.
Historical Perspective
Satellites first began in the mid-20th century when Sputnik was first launched by the Soviet Union in 1957. This marked the first time that a man-made device was sent into orbit, started the space race, and influenced the growing number of satellite launches ever since.
Early satellite data was rudimentary and analyzed manually because computer technology was highly limited at the time. Scientists would use photographs taken from space and compare them side-by-side to identify changes and patterns in the images. As technology evolved, these photographs were able to capture additional information including thermal, infrared, and ultraviolet readings using advanced lenses.
However, software limitations still made it difficult to analyze all of this information as satellites perpetually gathered information. The sheer volume of data made it difficult for scientists to analyze manually and caused a growing need for computer scientists. Storage was also a major concern because satellites were capturing data 24/7 and there was not enough space to store all of it without hard drives.
The Rise of AI in Satellite Data Analysis
AI has been prevalent in data analysis for decades now, going back as far as the 1980s when machine learning was first developed. So, it comes as no surprise that AI can be applied to the data captured by satellites. In fact, these AI systems are able to exponentially increase speeds over manual and computer-assisted data analysis methods, using algorithms that are trained to categorize and monitor real-time data captured on Earth or in space.
Deep Learning, and convolutional neural networks specifically, are perfect for image recognition, using grid-like data to understand information and put it into context for humans. These AI models can analyze vast quantities of subjects including natural habitats and biomes to logistical routes, using automated data processing to influence predictive analytics for improved results.
Benefits of Using AI in Satellite Data Analysis
There are a number of incredible benefits that AI brings to satellite data analysis. By using Deep Learning, AI can process vast amounts of information at exceptional speeds.
Accuracy and Precision: Human error can be a major burden, but AI models are able to process information without making common mistakes when training is done properly because computer systems don’t get fatigued, allowing them to maintain uniform accuracy.
Speed: Satellites capture massive amounts of data from Earth and even more information when looking into deep space. This creates a burden for scientists that can’t keep up with these large quantities of data. Fortunately, automation can process information at much higher speeds, giving scientists more time to focus on the data outputs instead.
Scalability: Satellites generate massive amounts of data and require AI models that can keep pace with the growing amount of information that needs processing. By integrating AI with cloud computing, raw data can be analyzed fast enough for real-time data to remain useful.
Predictive Analysis: AI systems have the unique ability to create predictions based on historical and real-time data. While humans can also do this, AI has the ability to identify and interpret subtle patterns that are easy for humans to overlook, giving them a heightened ability for accurate decision-making.
Applications and Use Cases
AI models can be used in various applications using satellite imagery, making them beneficial to many sectors on Earth because of their vast variety of data interpretations. They include:
Weather Forecasting: Meteorology is one of the best sciences for AI application because satellites can provide AI with comprehensive overviews of entire biomes, analyzing biomes with a multitude of lenses to create accurate forecasting.
Natural Disaster Monitoring: Satellites can capture early images of natural disasters such as tornadoes and hurricanes. AI can then quickly analyze this information to determine the severity and assists with evacuations.
Agriculture: Monitoring agricultural land can help farmers understand the health of their crops by analyzing atmospheric information, informing them with advanced metrics on crop yields they can expect during harvest.
Urban Planning: Urban sprawl has only gotten larger as more humans continue to be born and satellites are helping us analyze the impact that it is having on our habitable land. We can use AI to analyze images for potential development zones and understand which areas are best for new neighborhoods.
Space Exploration: Understanding our solar system has massive potential for humanity. AI can use satellite imagery not only to find suitable locations for exploration on the moon and Mars, but it also opens up the possibility of finding extraterrestrial life in deep space.
The Future of AI in Satellite Data Interpretation
With more advanced AI models being developed, satellite imagery has the potential to provide humanity with expansive insights into our world that can help us fight climate change, develop more efficient cities, and even explore the depths of space.
Advanced telescopes like the James Webb Space Telescope are already capturing some of the most incredible images of deep space with a level of clarity that we’ve never seen before. By orbiting the sun instead of Earth, we are able to gather a vast amount of new information using its unique array of lenses for AI to analyze and there’s no telling what we’ll find.
