How Satellite Imagery Dataset Used in Artificial Intelligence: AI Applications for Satellite Imagery

Satellite images can capture the wide-angle viewing area from the distant height making it easier for artificial intelligence applicable to various fields. And AI applications using the satellite images can learn to predict various scenarios after analyzing the situation from satellite imagery datasets that are specially created for AI models.

And to train such models a huge amount of training data set are required through machine learning or deep learning algorithms. These satellite images data includes different types of Aerial views of various things like agricultural fields, forest, cities for urban planning and management and various other manmade structures. So, today we will discuss right here how satellite imagery dataset used in artificial intelligence with AI applications.


Actually, in artificial satellites are manmade satellites, and AI is used in satellite imaging through two ways – One-level” applications and Multi-level” applications. Hence, to clarify both level applications, it’s important to discuss from a technical perspective, these applications require complex machine learning pipeline for various types of projects.

“One-step” Applications of Satellite Data

The first and most important applications of satellite imagery data are making the various objects detectable to computer vision from the space level heights. Objects like urban area boundaries are important for municipalities, buildings, road segments, government agencies, rescue teams, military, and other city agencies. And such things can be easily observed from space satellites through captured images taken from advanced cameras.

Also Read: How to Improve Computer Vision in Autonomous Vehicles using Image Annotation Services

“Multi-level” Applications of Satellite Data

Another crucial application of satellite data in AI developments is at the time of detecting the object the task has been allowed by several integrated ready-to-use pipelines using convolutional neural nets there are some issues with satellite imagery which make this task more challenging.

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Multi-level applications are ones in which information, extracted from satellite imagery is only a line of features in a more complex ML system. From detecting the cars in the parking area to crop yield prediction, oil inventory detection and monitoring the economic activity of some countries across the globe, especially – hard-to-reach ones, such as China.

Detecting the Cars in the Parking Area

Satellite images can count the cars in the parking zone and when these images are annotated and used as a training data to develop an AI-based algorithm to accurately detect and count cars on satellite imagery at parking lots across the globe and enhance this data using other sources, it’s possible to predict footstep count at the retailer’s shops.

Detecting the Cars in the Parking Area

Aerial Field Mapping of Agricultural Fields

Another application of satellite images data can help the AI model in crops’ yields and price prediction. The AI model builds on remote sensing data has proven a very successful tool for estimation yields. From the other side, it’s obvious that yield-related tasks cannot be solved only by vegetation indices, thus more features should be included, such as weather and soil-related data.

Aerial Field Mapping of Agricultural Fields

Also Read: Five Reasons Why You Need To Outsource Your Data Annotation Project

However, from the other side, it’s obvious that yield-related tasks cannot be solved only by vegetation indices, thus more features should be included, such as weather and soil-related data. Along with the huge quantity of satellite imagery training data gathered from such sources, if we augment these features with prices and sentiment analysis, it’s possible to create a model beating official forecasts in terms of timing and accuracy.

Estimating the Inventory of Oil Reserves

The third application of satellite images dataset is estimating the inventory level of oil by detecting the shadows on such tankers or oil-wells. This idea is pretty straightforward, as most of the fuel tanks have floating top, it’s possible to calculate it’s volume and the amount of in it, utilizing very simple trigonometry to estimate liquid level in a container.

Also Read: How to Create Training Data for Machine Learning

Estimating the Inventory of Oil Reserves

Actually, using the polygon annotation the shadows can be precisely annotated if locations of all such oil tanks detected through space satellites. And the data extracted from satellite imagery or gathered from some databases can calculate global oil inventories. Here oil and gas companies are the investors who can monitor competitors, probably government agencies managing oil reserves, interested in timely information on other countries’ energy resources.

Monitor the Economic Activities of Countries

Another important application of satellite imagery datasets is to monitor the economic activities of the various countries globally. Mainly for the countries hard-to-reach ones, like China. For example, the unusual activity at the actual line of control at the India-China border can be monitored through satellite images of Galwan valley where dispute started.

Monitor the Economic Activities of Countries

Also Read: Why Data Annotation is Important for Machine Learning and AI

Monitoring the images, if you can give an independent satellite-powered measure cities developments like car density, construction rates, electricity consumption through nighttime illumination including import and export operations or ship count and sizes, aircraft count in airports, combine it with traditional surveys, statistics and media data and, finally, pack it in some index, it can be valuable for investors and government agencies.

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Working closely with experts to provide data annotation solution for machine learning and AI at affordable cost. Expert in image annotation services and consistently working to improve the quality and accuracy while sharing the knowledge how to make available training data for AI and machine learning to developers worldwide.

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