IBKR Quant Blog


Deep learning in Satellite imagery

By Damian Rodziewicz, Appsilon Data Science


In this article, I hope to inspire you to start exploring satellite imagery datasets. Recently, this technology has gained huge momentum, and we are finding that new possibilities arise when we use satellite image analysis. Satellite data changes the game because it allows us to gather new information that is not readily available to businesses.


Why are satellite images a unique data source? What is currently available, and what properties do you have to take into account when choosing which images to use?

Satellite images allow you to view Earth from a broader perspective. You can point to any location on Earth and get the latest satellite images of that area. Also, this information is easy to access. There are free sources that allow you to download the mapped image onto your computer, and then, you can play with it locally.

One of the most important aspects of using satellite images is that you can also browse past images of certain locations. This means that you can track how the area changed over time and predict how it will change in the future. All you have to do is define the properties that are relevant to your use case.

To give you an idea of how satellites track our progress on Earth, we have to take a look at what is above us.


Source: European Space Agency

There are currently over 45 hundred satellites orbiting the Earth. Some are used for communication or GPS, but over 600 of them are regularly taking pictures of the Earth’s surface. Currently (as of end of 2018), the best available resolution is 25cm per pixel, which means that 1 pixel covers a square of 25cm x 25cm. This translates to a person taking about 3 pixels on an image.

The current technology we have actually allows us to get an even better resolution, but it is not available, as many governments don’t allow us to take more detailed images due to security reasons. Meaning, you won’t be able to access better quality unless you have security clearance.

Available sources of satellite images

The first group is free public images. Amongst them are American Landsat and European Sentinel, which are the most popular free images. Landsat will provide you images with a resolution of 30m per pixel every 14 days for any location. Sentinel will provide images with a resolution of 10m per pixel every 7 days.

There are also commercial providers, like DigitalGlobe, that can provide you with images with a resolution up to 25cm per pixel where images are available twice a day. It is important to strike a balance between the different properties that you need, as the best resolution doesn’t always mean that you get the most frequent images.

Also, cost is an important factor. The best images can cost up to a couple hundred dollars so it is wise to start building your solution with lower quality images. Just make sure you use the best ones for your particular use cases. Of course, commercial sources offer subscriptions, which will reduce the images’ cost.


Properties of satellite images

Let’s go through the properties that you have to balance out when choosing an image source. First is spatial resolution. As you can see, technology has been rapidly advancing, and there is more and more money being invested into launching better satellites and making them available.

The second factor is temporal resolution. This is how often you get a picture of a given place. This is an important aspect because of how clouds may block your point of interest. For example, if you only get 1 image every 7 days, and your location is in a cloudy area, then it is likely all your images in a month might be blocked by clouds, which stops you from collecting data in your area. There are some algorithms being created to mitigate this issue, however, it is still a big problem when browsing images. For the most part, it is better to get the highest possible frequency to improve your chances of getting a clean shot of the given area in the selected time frame.

Now, the third factor is interesting. It is spectral resolution. When you think about an image, you usually think of three layers: red, green, and blue; these layers compose a visual image of the area. This is because our human eye has three color-sensitive cones, which react to red, green, and blue.

Satelite Data


Visit Appsilon Data Science Blog to learn how to leverage satellite data source in our R projects, and to read the rest of the article:




About Appsilon  

Our Vision: To discover tomorrow’s applications of data & apply them today. We constantly improve how data is acquired, processed and used. We are driven by using Data Science at the forefront of business, leveraging the potential of the ever increasing amount of data. https://appsilon.com/



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