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Satellite images allow us to monitor changes in ecology, military, and other areas in real time. Add artificial intelligence (AI) tools to the process of analyzing satellite images and it speeds up by tens of thousands of times. In addition, the accuracy of data tracking with AI also increases.
In this article, we will consider technologies that power AI and use cases of AI in satellite image analysis. You will also discover what future awaits for us with the development of this innovative approach.
The Evolution of Satellite Imagery with AI
Satellite imagery has become much easier to analyze with the integration of artificial intelligence in recent years. Previously, this was a manual process, but technology has made the process and its accuracy much faster.
Limitations of manual interpretation vs AI satellite image analysis
Manual interpretation of satellite images is inferior to AI in speed, accuracy, and volume of analyzed data.
As the resolution and frequency of shooting increases, it is very difficult for analysts to maintain such a scale of analysis and not lose the quality of their work. Also, when a person analyzes the image, this often leads to errors.
What is another big advantage of AI in satellite imagery? Impartiality and objectivity. Let's not exclude that a person's assessment is subjective. It depends on the specialist's experience and many other conditions.
The role of AI in satellite imagery analysis
Traditional analysis methods rely on following predefined rules. AI, on the other hand, learns from huge sets of annotated image data. In this way, it continually improves, improving the speed and quality of analysis.
Modern AI models can automatically identify objects such as roads, bodies of water, and vegetation more consistently and quickly than human analysts.
Dive into the key technologies shaping generative AI
Technologies Behind AI-powered New Satellite Imagery Analysis
Machine learning (ML), computer vision (CV), and deep learning (DL) are the latest AI innovations helping to obtain accurate data from satellites.
ML models help get large amounts of image data to create algorithms using computer systems, and DL helps decode data successfully. CV assists in better analyzing the visual content of images.
Below, we will consider these technologies separately.
Machine learning
Machine learning is the core technology of many AI-driven systems used for satellite imagery analysis. ML algorithms are trained on huge labeled image datasets to recognize patterns. They need to be able to make decisions or predictions without being programmed to perform certain tasks.
As ML models can "learn" from huge amounts of data, they effectively recognize subtle patterns. Human analysts can miss this data. In such a way, ML is a necessary technology to make accurate image analysis.
Deep learning
Deep learning is a more advanced subset of ML. This technology includes neural networks with many layers (hence "deep"). Deep learning networks can hierarchically process data and learn complex representations from raw data. Applying this technology also helps to increase the accuracy of the final analysis results.
Deep learning comprises CNNs (convolutional neural networks), which detect and classify objects within images by scanning through them in a structured way. We advise using CNNs to detect changes in the environment and infrastructure. You can also use these networks to identify natural disasters and more. There are plenty of applications.
Computer vision
Computer vision allows machines to perceive visual information from the environment as humans do. CV algorithms help analyze images, recognizing visual objects, textures, and spatial relationships between all parts of the snapshot.
These CV models are easy to train to distinguish one object from another automatically. It makes the analysis process more efficient. The ability of computer vision to "see" and "understand" visual data is very important in urban planning and environmental monitoring.
Case Study: Effective Navigation Solution for UK-based Drivers
AI Use Cases in Satellite Data Analysis
Artificial intelligence has already found its way into plenty of sectors, from agriculture to the military.
Environmental monitoring
Track water pollution levels or check forest health in real time? AI can do it incredibly quickly. Global warming is now a pressing problem that scientists are studying. Now, scientists can already measure the size of icebergs using satellite photos and AI and monitor the melting rate of icebergs. AI has accelerated this process by 10,000 times the speed at which a human would do.
Agricultural insights
AI helps farmers and businesses monitor the health of crops. The technology can even predict the harvest amount, which helps distribute resources correctly.
German startup Peat has created an app called Plantix to detect crop diseases. Farmers can take photos of damaged plants, and an algorithm uses computer vision to generate the name and cause of the disease.
Military and national security
In the military sphere, analytics cannot do without the latest technologies, such as AI satellite image processing tools. These technologies make it possible to monitor critical objects.
In 2022, with the help of images from the BlackSky satellite software project, monitors confirmed the construction of a major Chinese naval base in Cambodia, whose existence had previously been denied by China.
Natural disaster management
AI technologies help identify locations where disasters such as fires, hazards, and other catastrophes occur. It is necessary for the quick response of rescue services and for assessing the extent of damage.
Experts at the USC (University of Southern California) have created an AI model to accurately predict the spread of wildfires using satellite imagery. The technology will be a breakthrough in fighting wildfires and responding to emergencies, Tech Xplore reports.
Urban planning and infrastructure
Satellite images can reveal many interesting things. These are the demolition of buildings, construction of temporary structures, construction of permanent facilities, or traffic jams.
Our case study
At Requestum, we can boast a wide range of successful AI and data science software solutions we developed. One of our outstanding projects is a solution that detects cars on overhead images.
What was the key aim of our development efforts?
The client's vision was to create a solution that would help calculate car density in a selected region. Why? It had to assist people with urban and smart city initiatives, such as planning and assessing road traffic.
Below, we mentioned what we have implemented within this project:
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Object detection feature: The You Only Look Once (YOLO) object detection system helped us to create advanced CV algorithms. The solution can accurately detect and count autos within diverse images;
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Map tiles processing capabilities: With the help of the Google API, we handled map tiles. Thus, we got detailed geographical data to pinpoint the areas where car density must be calculated;
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Map visualization tool: The Leaflet.js tool lets us showcase the autos density data on interactive maps. So the presented information is easily accessible and clear.
You can try a demo of this project on our website to see how the solution works.
Ethical and Operational Considerations in AI Satellite Image Analysis
There are no shortcomings or difficulties in applying AI technology. However, this is different. The human traits of error and subjective judgment, which machines eliminate, also can be our advantage over them.
AI software still has a long way to go to the level of human empathy and understanding our ethical boundaries. And it is unknown whether machines can think exactly like us, only faster.
Let’s take a look at the operational points that also need to be considered when using AI technologies.
Privacy and data security
When using AI to analyze satellite imagery for any field, it is important to maintain data privacy and security. The accuracy of satellite imagery raises serious concerns about data privacy, especially when tracking activity in politically sensitive or private areas. It may be that actions are considered surveillance abuse.
Organizations that use AI to analyze satellite imagery must follow strict protocols for storing and transmitting data. It is also important to use encryption and anonymization methods.
Human control and bias reduction
Human oversight is still necessary despite the advanced capabilities of AI. To ensure that algorithms function correctly, they need to be monitored and corrected for errors so that they learn from them. Human resources are also needed for ethical oversight. Specialists will help ensure that AI systems do not violate human rights or target vulnerable populations.
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Scalability and cost-efficiency
One of the most essential benefits of using AI in satellite image analysis is its scalability and cost-effectiveness. At least, companies won't have to pay salaries to as many employees. It also raises debate about how far AI will advance before organizations no longer need humans.
Conclusion
Now, relatively simple AI processes like ML can highlight things like auto density or the location of a fire. Spotting trends in large volumes of imagery still relies heavily on humans.
In the future, AI systems will be able to perform even more complex image analysis in minutes. The National Geospatial-Intelligence Agency considers AI as "absolutely the future." That's a claim made by Mark Munsell, who leads the agency's Data and Digital Innovation Directorate.
Contact us if you want to create an AI startup and stand out in the market while the AI field is gaining momentum. We have extensive experience in developing top AI and data science projects.
Our team is dedicated to delivering high-quality services and achieving results that exceed clients' expectations. Let’s discuss how we can help your business succeed.
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