European Space Imaging Delivers 100% Success Rate of European Commission’s CwRS Program

Early last week European Space Imaging finalised the collection of close to 400,000 square kilometers of Very High Resolution (VHR) satellite imagery for the European Commission. This marks the completion of its VHR Image acquisition for the 2019 Controls with Remote Sensing (CwRS) program with a 100% success rate for the third year in a row – demonstrating once again the company’s credibility and reliability as Europe’s leading VHR satellite image provider.

“We are extremely proud to continue our success streak of 100% in this vital and often demanding campaign,” said Adrian Zevenbergen, European Space Imaging’s Managing Director. “Our staff worked incredibly long and hard to acquire the data in just 50 days.”

The CwRS program monitors agricultural land for which farmers have been granted subsidies under the EU’s Common Agricultural Policy (CAP), amounting to approximately €40 billion per year. European Space Imaging has been the major provider of Very High Resolution satellite data to the program since 2003.
European Space Imaging had 714 acquisition windows for each of the 628 agricultural zones spread all over Europe using the WorldView-2, WorldView-3 and GeoEye-1 sensors. This year, cloudy weather over Northern Italy, the United Kingdom and Ireland made the operation particularly challenging.

“Despite weather challenges, we managed to collect images for approximately 63% of the zones completely cloud free. Due to operating our own ground station we were able to deliver the imagery in near-real time, thereby assisting and enabling the EU Member States to manage their own deadlines,” said Dr. Melanie Rankl, Project Manager at European Space Imaging.

From its ground station in Munich, European Space Imaging takes into account real-time weather conditions before directly tasking the world’s most advanced satellite constellation: WorldView-1, GeoEye-1, WorldView-2 and WorldView-3. Direct tasking allows the company to minimise cloud cover, increase collection efficiency, enable customer flexibility and shorten delivery times.

About CwRS

Since 1993, the European Commission (EC) has promoted the use of “Controls with Remote Sensing” (CwRS) as a system to control agricultural subsidies granted under the EC’s Common Agricultural Policy. Following the real time evaluation in 2003 and the successful operational application since 2004, the EC’s Joint Research Centre (Director General (DG) JRC), in agreement with DG AGRI, provides VHR satellite remote sensing data to the EU Member States (MS) administrations for their CwRS area-based subsidies.
The DG JRC provides technical guidance regarding the CwRS methodology as well as managing the image acquisition, ordering and communication with the member state administrations and image providers. Also, in close cooperation with the member states, it supports the definition of the imagery required. Satellite imagery is acquired through third party suppliers selected by the DG JRC like European Space Imaging who has been a key third party imagery supplier to these campaigns since the programs’ inception.


Find out more about the CAP: https://ec.europa.eu/agriculture/cap-overview_en

Share on facebook
Share on twitter
Share on linkedin

Related Stories

Scroll to Top

Architecture of ResNet34-UNet model

UNet architecture for semantic segmentation with ResNet34 as encoder or feature extraction part. ResNet34 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

Architecture of VGG16-UNet model

UNet architecture for semantic segmentation with VGG16 as the encoder or feature extractor. VGG16 is used as an encoder or feature extractor in the contracting path and the corresponding symmetric expanding path predicts the dense segmentation output.

Architecture of ResNet34-FCN model

In this model, ResNet34 is used for feature extraction and the FCN operation remains as is. The feature of ResNet architecture is exploited where just like VGG, as the number of filters double, the feature map size gets halved. This gives a similarity to VGG and ResNet architecture while supporting deeper architecture and addressing the issue of vanishing gradients while also being faster. The fully connected layer at the output of ResNet34 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

Architecture of VGG16-FCN model

In this model, VGG16 is used for feature extraction which also performs the function of an encoder. The fully connected layer of the VGG16 is not used and instead converted to fully convolutional layer by means of 1×1 convolution.

X