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.
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:
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/
This article is from Appsilon and is being posted with Appsilon’s permission. The views expressed in this article are solely those of the author and/or Appsilon and IB is not endorsing or recommending any investment or trading discussed in the article. This material is for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IB to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.
We appreciate your feedback. If you have any questions or comments about IBKR Quant Blog please contact firstname.lastname@example.org.
The material (including articles and commentary) provided on IBKR Quant Blog is offered for informational purposes only. The posted material is NOT a recommendation by Interactive Brokers (IB) that you or your clients should contract for the services of or invest with any of the independent advisors or hedge funds or others who may post on IBKR Quant Blog or invest with any advisors or hedge funds. The advisors, hedge funds and other analysts who may post on IBKR Quant Blog are independent of IB and IB does not make any representations or warranties concerning the past or future performance of these advisors, hedge funds and others or the accuracy of the information they provide. Interactive Brokers does not conduct a "suitability review" to make sure the trading of any advisor or hedge fund or other party is suitable for you.
Securities or other financial instruments mentioned in the material posted are not suitable for all investors. The material posted does not take into account your particular investment objectives, financial situations or needs and is not intended as a recommendation to you of any particular securities, financial instruments or strategies. Before making any investment or trade, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice. Past performance is no guarantee of future results.
Any information provided by third parties has been obtained from sources believed to be reliable and accurate; however, IB does not warrant its accuracy and assumes no responsibility for any errors or omissions.
Any information posted by employees of IB or an affiliated company is based upon information that is believed to be reliable. However, neither IB nor its affiliates warrant its completeness, accuracy or adequacy. IB does not make any representations or warranties concerning the past or future performance of any financial instrument. By posting material on IB Quant Blog, IB is not representing that any particular financial instrument or trading strategy is appropriate for you.