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Sentinel Hub

Sentinel Hub – The Cloud API for Analysis Ready Satellite Data

 

Gregor Milcinski is the CEO and co-founder of Sentinel Hub and has worked in the geospatial field for about 20 years. He shares his knowledge of the Earth observation industry and gives us an in-depth explanation of what it is that the Sentinel Hub does.

Sentinel Hub is a cloud API for satellite imagery. It uses its APIs to enhance access to satellite data from missions such as Sentinel, Landsat, and other commercial satellite projects. Sentinel Hub users make use of its APIs to process the data they are interested in, and access it in a format that best provides the information they need.

This helps take off the operational load in terms of time and effort required to process the large, ever-changing volumes of satellite data. Sentinel Hub enables users to obtain Analysis Ready Data (ARD).

This means having data in a form that is ready for a particular workflow process and does not require additional cleaning. Sentinel Hub estimates that the capabilities they provide cover about 80% of what users may want to do as far as data processing is concerned.

Among others, these include Ortho-rectification, data transformations, rescaling, re-projection, as well as applying some machine learning models. Using a simple API request, users can get immediate access to full and global archives of the relevant satellite missions partnered in Sentinel Hub.

 

A Peek into How Sentinel Hub was Built

The idea that eventually transformed into Sentinel Hub was born from operational difficulties in a previous project. This triggered the realization that the technology which was already available for working with satellite data was not overly suitable for processing the ever-expanding, continually updating, large volumes of data collected by satellites. A prototype was developed and subsequently iterated to evolve into the now Sentinel Hub.

This journey of growth is an inspiration to those who may want to build something, but are unsure on how they should start. Do not get stuck in the idea that you have to start big straightaway. Start where you are confident, and once you figure out what is working, keep building on it.

 

How Sentinel Hub Works

Sentinel Hub works ‘on the fly’. It does not store pre-processed data that can be ordered off-the-shelf. Rather, the processing happens on the fly as requested by the user. This approach was chosen since it is quite impossible to accurately predict what a users’ needs will be in terms of geographical, temporal, and spectral aspects.

This is also one of the reasons why Sentinel Hub does not cache processed results (apart from privacy policies). Considering these aspects, there is a high risk of storing petabytes of data that may never be needed. Instead, Sentinel Hub worked to optimise their processing steps as much as possible for speed.

Currently, it only takes a couple of seconds for the most usual requests (like above) to process. This is typically fast enough that users can integrate these APIs directly in their applications in an interactive manner, without even having to store the data on their side.

Keeping operations to “on-the-fly” also allows Sentinel Hub to expose their new capabilities and improvements to their users by simply deploying a new version.

If data was pre-processed, there would be a fixed dataset which would need processing in order to introduce the new improvements. This could be expensive, time-consuming, and inconvenient.

 

How is Sentinel Hub Different?

The description of what Sentinel Hub does may have you thinking that it is the same as Google Earth Engine, or Microsoft’s Planetary Computer. Ideally, it actually falls somewhere in between the two. See the sections below to see how they compare.

 

Sentinel Hub vs Google Earth Engine

Google Earth Engine was designed as a search platform where people could do analyses, and then make use of the results. It was not primarily designed to power other applications.

Even though there are workarounds regarding this, Google Earth Engine was mainly designed for people to work with the results of its analyses. On the flip side, Sentinel Hub is designed to power other applications.

It offers a set of APIs which users can easily integrate into their procedures and workflows. It removes limitations on what users can do, and where they can do it. Building your workflow on top of Sentinel Hub, you can do just about everything, since it is possible to fine-tune what you’d like to get from Sentinel Hub APIs.

 

Sentinel Hub vs Microsoft’s Planetary Computer

Microsoft’s Planetary Computer can be viewed as a platform that is mainly for making geospatial data available in a cloud-native way. It does not abstract the complexity of satellite data, which makes it difficult to perform tasks like stitching scenes. Microsoft’s Planetary Computer provides APIs to access the metadata of the satellite data for users’ workflows being run on virtual machines in Azure. This in itself is very limiting for users who may want to run it on a different cloud. On the other hand, Sentinel Hub allows users to be more flexible as they can use the program in their own environments.

Businesses that have their own infrastructures and proprietary data that they do not want to use outside their environments can simply use Sentinel Hub as a data stream in their procedures from wherever they are.

 

Who Are the Users of Sentinel Hub?

The vast majority of Sentinel Hub users are application developers and data scientists. The application developers are mostly those working in the agricultural sector.

One reason for this is that Sentinel-2 provides a powerful collection of data with a resolution that is most useful for agriculture and vegetation monitoring. Typically, many of the Precision Agriculture applications are powered by Sentinel Hub.

The use of Sentinel Hub by data scientists leans towards agriculture primarily for monitoring activities and climate change analyses. Data scientists are the largest consumers of Sentinel Hub since they perform large scale operations using machine learning. Despite making up only 5% of the paid users of Sentinel Hub, data scientists consume about 80% of the volume of data.

Apart from the agricultural sector, Sentinel Hub also serves the energy, defence, environment, and mining sectors.

 

Partnering with Sentinel Hub

Satellite companies can partner with Sentinel Hub through its “Bring Your Own Data” service. This service allows satellite companies to plug in their data to Sentinel Hub in any cloud native format supported by Sentinel Hub (i.e. GeoTIFF, XAR), and basically any kind of raster data. Sentinel Hub does not copy this data, but merely stores the metadata in their database for faster access, like a catalog.

Users can then make use of the same set of features offered by Sentinel Hub to distribute data efficiently. The satellite providers are responsible for charging users for using their data as Sentinel Hub does not support this functionality at the moment.

 

Sentinel Hub’s EO browser

The EO browser is a Google Maps-type application that runs in a browser. It provides access to the satellite missions supported by Sentinel Hub, and is free for non-commercial use. In the EO browser, you can check for the most recent data in any part of the world and visualize it interactively (i.e. zooming in and out, viewing different band combinations, doing time lapses, switching between satellites), and much more. No account is needed to use these capabilities in the browser.

The EO browser showcases what Sentinel Hub’s APIs can do when integrated into workflows. It is a perfect display of how satellite data can be used. If you want to get familiar with or know more about satellite data, the EO browser is a useful resource for that.

 

An Overview of the Earth Observation (EO) Industry

The usefulness of satellite imagery has grown over the years as experts explore more applications where the use of satellite data would improve efficiency, and insights. Coupled with developments in IT and data capture technologies, the Earth Observation industry has exposed new opportunities but also faces some setbacks.

 

Opportunities in the EO industry

Spatial resolution in satellite imagery has improved in recent years and has unlocked new opportunities for using satellite data. Some satellite missions, such as Sentinel-2, have launched a revolution by providing very useful, good quality, free data set that can be used as the basis for a number of geospatial workflows.

Machine learning is also worth mentioning due to its contribution to the EO industry. It has revolutionized the interaction with the normally large volumes of satellite data, and processing it. There is still much to be done in terms of developing machine learning procedures for highly dynamic satellite data, and the wide variety of more niche uses that consumers crave.

 

Setbacks in the EO Industry

The complexity in the nature of satellite data, coupled with the unique traits of geospatial applications introduces difficulties in using machine learning to fully realize opportunities that emerge in the EO industry. This uniqueness hinders the development of off-the-shelf machine learning algorithms. There are a very limited set of applications for which machine learning procedures can be offered as a service (i.e. ship or building detection). In most other cases, there would still be a need to tailor the procedure to a specific workflow or niche.

In an effort to contribute to the development of machine learning in geospatial applications, Sentinel Hub have open-sourced all their internally developed machine learning procedures, and hosted them on GitHub. They allow other people to refine these procedures and use them as a starting point for their own machine learning projects.

Making the decision on how to balance temporal and spatial resolutions during data capture is also a defining line on whether the missions will generate sufficient value.

For instance, applications in the construction industry may require high spatial resolution (i.e. 1 meter) as well as a high temporal resolution (i.e. 1 day) to monitor construction progress. For applications like vegetation monitoring where there will be no significant change within a day, using these exact resolutions will be overkill.

It is highly likely that the value generated may not be sufficient to compensate for the cost used in processing.

Another challenge is the fact that satellite data is only periodically produced. It is unlikely that a user can find what they want in their area of interest at the particular time that they are interested in. A satellite can only be tasked for the future, not the past.

Advancing the Earth Observation Industry

Sharing knowledge has always been one of the pillars for new developments that can take any industry to the next level.

In the geospatial field, more and more companies are cultivating the culture of sharing knowledge. Sentinel Hub is already making strides in this by sharing a lot of what they do (i.e. openly sharing their machine learning procedures, which they could easily choose to make proprietary).

Sentinel Hub explained that they are doing this because they believe the Earth Observation field is in a phase where contributing to its growth is more important than growing one’s position within it.

The Future of the Earth Observation Industry

Growth is expected in the Earth Observation industry for the foreseeable future. With the introduction of the monitoring approach in almost all elements of our lives, satellite data will be needed since it is a rich, objective, and useful primary source.

The industry will grow since this data will be needed in a form that makes it easier to extract information, and be processed it in a smart way. Sentinel Hub acknowledges that they are only doing a tiny part in the industry. It will need effort from the geospatial community to grow all the tiny parts and realize growth across the entire industry.

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