On this page you can find all publicly available deliverables and reports from GEM project, once they have been accepted. 


Deliverable 2.5 - Integration of VHR sources

New data gateways have been developed to allow the ingestion of very high-resolution data via Sentinel Hub. A list of supported sources is:

• Planet's PlanetScope data

• Airbus's Pléiades data

• Airbus's SPOT data

• Maxar's WorldView data

The deliverable shows a detailed description of how to use, purchase, order, and access data for the supported sources. One or more images are added for each supported source, with an example of how the data looks like. A tutorial on how to search and order data from third-party providers such as PlanetScope or Pléiades using Sentinel Hub API with Requests Builder is also added.

link to deliverable


Deliverable 4.2 - Cloud masking

As part of the Machine Learning Package (WP4), this deliverable (D4.2: Cloud masking) demonstrates the development and implementation of cloud masking algorithms that were included in eo-learn to enable pseudo-probability cloud masking of Sentinel-2 data. The cloud masking technique is integrated into the Sentinel Hub's pre-processing chains and made readily available for the complete Sentinel-2 archive.

We have upgraded the eo-learn library with two cloud masking algorithms: s2cloudless for single-observation cloud masking and InterSSIM for multi-temporal cloud masking. The deliverable also shows how the s2cloudless model is used to produce cloud masks and accompanying cloud mask pseudo-probabilities accessible directly through Sentinel Hub service. This gives users a more streamlined access to the data, directly suitable for value added services, since they do not have to deal with cloudy pixels anymore.

For a quick demonstration of the cloud masking capabilities, reader can go to EO-browser application using this link http://bit.ly/eobrowser.

link to deliverable


Deliverable 4.3 - eo-learn Gateways

In this deliverable we show the integration of eo-learn with popular machine learning and deep learning frameworks. In particular, we demonstrate the integration with conventional ML frameworks already in place, and is being used extensively.

From the numerous deep learning frameworks, we explain why we decided to interface eo-learn with two: TensorFlow and PyTorch. In order to avoid dependency, maintenance and implementation issues, we have decided that the gateways – interfaces between eo-learn and deep learning frameworks – will be developed as standalone packages. The integration with eo-learn and implementation of a number of deep learning models using TensorFlow framework has already been released in the eo-flow package. In the continuation of the Global Earth Monitor project, PyTorch interface will be added in a similar fashion, extending eo-learn workflows to another large deep learning community.

link to deliverable


Deliverable 6.1 - GEM portal

The Global Earth Monitor portal will make all available public products and documents accessible to a wider user community than those already involved in the project. Sinergise has set up the project website https://globalearthmonitor.eu and will later manage it throughout the project.

link to deliverable