On the flip side, Sentinel Hub is designed to power other applications. It was not primarily designed to power other applications.Įven though there are workarounds regarding this, Google Earth Engine was mainly designed for people to work with the results of its analyses. Google Earth Engine was designed as a search platform where people could do analyses, and then make use of the results. See the sections below to see how they compare. Ideally, it actually falls somewhere in between the two. 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. This could be expensive, time-consuming, and inconvenient. If data was pre-processed, there would be a fixed dataset which would need processing in order to introduce the new improvements. 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. 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.
Instead, Sentinel Hub worked to optimise their processing steps as much as possible for speed.Ĭurrently, it only takes a couple of seconds for the most usual requests (like above) to process. Considering these aspects, there is a high risk of storing petabytes of data that may never be needed. This is also one of the reasons why Sentinel Hub does not cache processed results (apart from privacy policies). 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. Rather, the processing happens on the fly as requested by the user. It does not store pre-processed data that can be ordered off-the-shelf. Start where you are confident, and once you figure out what is working, keep building on it. Do not get stuck in the idea that you have to start big straightaway. This journey of growth is an inspiration to those who may want to build something, but are unsure on how they should start. A prototype was developed and subsequently iterated to evolve into the now Sentinel Hub. 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. The idea that eventually transformed into Sentinel Hub was born from operational difficulties in a previous project.
SENTINEL HUB PIXEL PICKER FULL
Using a simple API request, users can get immediate access to full and global archives of the relevant satellite missions partnered in Sentinel Hub. 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.Īmong others, these include Ortho-rectification, data transformations, rescaling, re-projection, as well as applying some machine learning models. This means having data in a form that is ready for a particular workflow process and does not require additional cleaning. Sentinel Hub enables users to obtain Analysis Ready Data (ARD). 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 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.
It uses its APIs to enhance access to satellite data from missions such as Sentinel, Landsat, and other commercial satellite projects. Sentinel Hub is a cloud API for satellite imagery. 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. Gregor Milcinski is the CEO and co-founder of Sentinel Hub and has worked in the geospatial field for about 20 years. Sentinel Hub – The Cloud API for Analysis Ready Satellite Data
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