Our Partners

Space markets are inherently global, and that’s why European Space Imaging has a long-standing network of relationships with partners from all over the world.

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Maxar owns the most sophisticated satellite constellation in orbit, delivering images that give its customers the confidence to make the decisions that matter most. Its WorldView constellation is the only one to deliver true 30 cm resolution imagery, and collects over three million square kilometers a day, giving you access to all parts of the globe. With a very high resolution image archive that stretches back to 1999, and an innovative range of cloud-based image solutions, Maxar sets the standards for the rest of the industry.

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The German Aerospace Center (DLR) in Oberpfaffenhofen near Munich is the national research center for aeronautics and space. It is a superior satellite ground station operator with vast experience in all aspects of aerospace technology. Because of their expertise they make the perfect partner for co-ordinating the operations at European Space Imaging’s Direct Access Facility (DAF), which communicates directly with the satellites in the WorldView constellation.

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GAF AG is based in Munich and has an international reputation as an experienced provider of project design, management and implementation services in the fields of geo-information, satellite remote sensing, spatial IT-consultancy and capacity building for private and public clients. GAF provides solutions to clients across a wide variety of sectors. Over the past 29 years, GAF has been active in more than 100 countries throughout Europe, Africa, South America and Asia. They work closely with European Space Imaging on a daily basis.

Based in Abu Dhabi, Space Imaging Middle East is a leading global provider of commercial, high-resolution, world imagery products and services. Its imagery solutions support a wide variety of uses from mapping and analysis to navigation technology. Space Imaging Middle East has established itself as the most trusted imagery provider in the region, consistently delivering customised, cutting-edge geospatial solutions on time and under budget.

Satellogic is the first vertically integrated geospatial analytics company, driving real outcomes for its customers with planetary-scale insights they can trust. Satellogic’s low-Earth-orbit satellite constellation, platform, and data science teams work together to deliver end-to-end solutions at the right cost. The company makes sense of the data so customers can focus on the big decisions at hand.

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Ecopia.AI leverages artificial intelligence to convert high-resolution images of our earth into high-definition (HD) Vector Maps. These maps form a digital representation of reality and are embedded into critical applications, offering unique insight for decision-making at scale. Ecopia has created a powerful and growing database of HD Vector Map content around the world.

Reseller Network

We understand that our sales region has a rich cultural heritage, and that the needs and languages of our customers are diverse – that’s why we have developed an extensive reseller network across Europe, North Africa, and the CIS Countries. 

It’s through these unique partnerships, that European Space Imaging is able to offer solutions like no other satellite imagery provider.

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Interested in Partnership?

We are always searching for innovators in geospatial services.

Association memberships

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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.

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