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Guide to Machine Learning and Satellite Imagery

August 31, 2022

What is machine learning?

Machine learning (ML), which is a form of artificial intelligence (AI), allows software programs to predict outcomes more accurately without having to be explicitly instructed to do so. To predict new output values, machine learning algorithms use historical data as input. Machine learning is important because it can help in the development of new goods and provides businesses with an idea of trends in an accurate manner. The way in which a prediction-making algorithm learns to improve its accuracy is a common way to classify traditional machine learning.

What is satellite imagery?

Satellite images are photographs of the Earth taken by imaging satellites that are run by governments and companies all over the world. They are also known as Earth observation imagery and space borne photography. In general, satellite imaging is used to monitor and quantify natural and human activity on Earth. To take precise pictures of our moon, the satellites can also be turned and altered. The world can be shown in big or tiny scales using satellite photography, from a few streets to a whole hemisphere.

AI applications of satellite imagery

In order to collect the relevant data for creating AI models for related disciplines, machine learning training is expanded into many stages in AI applications for satellite images. The training data is required to recognize numerous things from such heights for field mapping or urban planning using AI models generated through machine learning or deep learning.

There are two types of applications.

1. "One-level" applications

"One-level" applications, which directly integrate machine learning methods with satellite imagery This comprises item detection and change detection, which have many uses in the agricultural industry, land use, and urban infrastructure, among others.

2. "Multi-level" applications

"Multi-level" applications, which are pipeline-based programs built when data gleaned from satellite imagery is just a collection of features in more complex models, are also available. In "Multi-level" applications, or pipeline-based applications, data from "non-satellite" sources is consumed along with information gleaned from satellite imagery as just a set of features in more complex models. Examples include forecasting retail sales by counting the number of cars in the parking lots, predicting agricultural yields and prices, and estimating the world's oil supply by looking for shadows on objects

The challenges of using deep learning with satellite imagery 

1. More people than ever have access to satellite imagery, but higher-quality imagery with resolutions that allow for object detection is expensive to obtain. And if you contact the biggest service providers, such as Maxar, Planet, Sentinel, or Google Earth, it can be difficult to receive a response to your questions when you're interested in a modest project or collaboration.

2. The difficulty in managing trillions of bytes of unprocessed satellite data Often, imaging satellites are designed to collect data on certain subjects, thus the data doesn't come in the form of tidy, organized pictures like snapshots from a photo studio. It's unprocessed data, a sea of binary data. Researchers who access the data must be aware of their objectives.

3. Accessibility is a problem because, inevitably, using satellite photos is mostly restricted to researchers or organizations in developed countries.

Satellite Imagery and machine learning moving forward 

Aiming to address significant issues at scale, proponents of satellite imaging and machine learning have lofty goals. The technology has the potential to aid in anti-poverty initiatives, safeguard the environment, provide street addresses for billions of people, and boost food yields in the face of escalating climate change. A UNESCO report released this spring lists 100 artificial intelligence (AI) models that have the potential to improve society. But despite recent improvements in deep learning and the quality of satellite imagery, as well as the record number of satellites expected to enter orbit over the coming years, ambitious attempts to use AI to solve large-scale problems still run into conventional obstacles like bureaucracy in the government or a lack of political will or resources.

Conclusion.

Today, the ability of the commercial and scientific communities to evaluate satellite images is still greatly outstripped by its availability. For addressing issues as broad and significant as assessing the consequences of climate change, forecasting crop yields, and tracking global progress toward the Sustainable Development Goals, information found in satellite images is an essential asset.

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