openEO

openEO develops an open API to connect various clients to big EO cloud back-ends in a simple and unified way.

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H2020 grant 776242

17 March 2018

openEO proves its concept

by Edzer Pebesma, the openEO consortium

The problem

Earth Observation data are becoming too large to be downloaded locally for analysis. Also, the way they are organised (as tiles, or granules: files containing the imagery for a small part of the Earth and a single observation date) makes it unnecessary complicated to analyse them. The solution to this is to store these data in the cloud, on compute back-ends, process them there, and browse the results or download resulting figures or numbers. But how do we do that?

The aim of openEO is to develop an open API to connect R, python and javascript clients to big Earth observation cloud back-ends in a simple and unified way.

With such an API,

Why an API?

An API is an application programming interface. It defines a language that two computers (a client and a server) use to communicate.

The following figure shows how many interfaces are needed to be able to compare back-ends from different clients, without an openEO API:

api

With an openEO API (dark blue), the situation becomes much easier:

api

However, existing back-ends need to be taught to work with the new API, and clients that interact with back-ends need to be developed.

The task of the openEO project is to design, develop, and evaluate an API for cloud-based Earth Observation data processing.

First results

The openEO project started in Oct 2017. Now, within 6 months, we finished the first proof of concept, and demonstrate it. Three use cases were selected for this, three clients were developed pretty much from scratch (Python, R, and JavaScript), and seven back-ends were built or interfaced. Full information is available from the projects github organisation, and we point here to the

The three use cases

The three use cases comprise

  1. Derive minimum NDVI measurements over pixel time series of Sentinel 2 imagery
  2. Create a monthly aggregated Sentinel 1 product from a custom Python script
  3. Compute time series of zonal (regional) statistics of Sentinel 2 imagery over user-uploaded polygons

The full description, including the consecutive interaction steps of the API, is found on the

Links to the client and back-end implementations

Clients:

Back-ends

With all this, you can install your own back-end of choice, install a client, and start analysing the data! (but do read “Next steps”, below). Alternatively, we show a couple of videos of screen casts made while testing the various clients and back-ends.

Proof-of-concept videos

R client and WCPS back-end, use case 1

Grass GIS back-end, use cases 1, 2 and 3

R client and R back-end, use-case 1

R client and R back-end, use case 3

openEO Web Editor (JS client) with three back-ends (use case 1)

Demonstrates use of back-ends Sentinel Hub, WCPS EURAC, and OpenShift EODC

openEO Web Editor (JS client) with R back-end (use case 3)

Python client with GeoPySpark back-end (use case 1)

openEO Web Editor (JS client) with Google Earth Engine back-end (use case 1)

Links to documents

The following four documents (formal project deliverables) describe the proof-of-concept more extensively:

In addition, a document has been written that describes standards and interfaces used by the various back-ends, and the extent to which they are or will be useful for further developing the openEO API:

These deliverables have been submitted, but have not been approved, and hence may not be final.

Next steps

During the proof-of-concept, intentionally a number of difficult issues were not addressed, including

The next steps will include:

Getting involved

As any API, the openAPI will only become good and useful if it is being used, and it needs testing by a wide audience. We are now at the stage of designing it, but at the point where large chunks are useful already. If you want to contribute to any of this, please do not hesitate and contact us e.g. by