There are more than a thousand satellites orbiting earth and providing us with data every second. The information gathered from these satellites is quite useful for scientists, businesses and governments to make better decisions in all kind of industries!
In this article, I will tell you about some of the challenges faced by developers when it comes to building solutions related to Earth Observation and how Machine Learning powered with satellite data can help solve them. Also we will illustrate how you can build satellite data powered solutions using our simple Python Library!
At the top of the list is a lack of internal expertise around satellite data. In many cases, you must rely on third-party tools and services that very often deliver standardized output. However, you might require to customize your pipelines and workflows to your business and challenges, right ?
Another challenge is the need for one-stop-shop solutions that can process satellite data quickly and efficiently in order to streamline their analytics processes.
Finally, the Product team's priority is always scalability, evaluating how your solution's architecture stacks up against current and future expectations. At SpaceSense, we designed a platform that can deal with massive fully-customizable data workflows, maintain it and properly operate your satellite data powered solutions.
I won't teach you something new if I say that Python is the most popular programming language for data science, machine learning and AI. It has a huge community of developers who contribute to its open source projects.
SpaceSense enables users to keep control over what is happening by intertwining our Python library with their internal code - and as a result, keeping their codebase clean, maintainable and easy to understand. This is particularly important for large teams where it is crucial that all members of the team can access and understand the codebase in order to make changes or add features.
Our framework provides you with the building blocks necessary to build machine-learning ready datasets. You can use our pre-built components or create your own. It also allows you to easily integrate custom code into existing projects without having to worry about dependencies and other complexities of managing multiple libraries.
Our library is modular and highly scalable. We have designed it to be flexible enough to support both simple and complex projects, while also being fast and lightweight. It comes with a powerful pre-processing engine that allows you to load images into our system and extract the features you need.
The library we use for preparing your datasets for machine learning does not require any post-processing steps - saving your team time and efforts. We want to empower you as data scientists and engineers to build products that matter - and Python helps us achieve this goal. SpaceSense's mission is to make satellite data more accessible through a user-friendly interface that allows anyone to get started with satellite imagery without having to deal with complicated pipelines and workflows.
And now let's show you what power can you unlock with a simple Python Library.
Our platform provides the tools, support and resources to help you build and deploy machine learning models using satellite data. The platform is designed to be flexible and allow you to access the data in a variety of ways, including through an API or one of our pre-built client templates.
SpaceSense is scalable and can be used by small, medium and large organizations in order to solve their business problems with machine learning solutions that are powered by satellite data. OK, don't believe me for it, let's look at one of our many success-stories.
Since 2020, SpaceSense supports xFarm in its mission to help farmers in their work in a simple and visual manner, currently servicing over 30 000 farmers in Europe.
Originally, SpaceSense supported xFarm in defining which satellite data would provide the most value to their growers and increase. Xfarm started with integrating insights on crop health and crop performance for regular monitoring of fields on a large scale and built a crop input optimization solution to reduce costs of fertilization, seeding and pesticides combining crop health insights and other tools from SpaceSense.
This first of many projects in collaboration with SpaceSense, led xFarm to offer 3 new products leveraging satellite data across Italy within less than 3 months and resulted in 1000 new paying clients.
More information here: xFarm case study: The best way to get started with satellite crop monitoring.
Here is how you can build satellite data powered solutions like xfarm did in few lines of python & a few minutes.
Step 1:
Get access to usable, ready-to-use satellite data from Sentinel-2 (satellite data constellation most commonly used for crop monitoring) from a simple command:
Step 2:
Understand right crop health indicators you need for your monitoring to find the “vegetation indices” you need. Here is a quick description on the most interesting vegetation indices for crop health.
Select the vegetation indices with one line of Python code:
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Step 3:
Convert the data into insights your grower understands, either for a in-field variation on each day like this:
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or click here if insights on growth patterns over time are required.
With SpaceSense, you can build machine learning models with satellite data using Python. Our platform allows you to quickly get started with a variety of different algorithms and datasets without having to worry about the underlying infrastructure or codebase. Plus, we have a great community of users and developers who are ready to help you get started.
If you're interested in learning more about how SpaceSense can help your business, please contact us. We're excited to see what you build!
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