Satellite Images Show Europe’s Diversity in New Book

European Space Imaging have partnered with EOVision to produce a book entirely comprised of satellite imagery showcasing the diversity of Europe as seen from space.

The book titled Europa – Kontinent der Vielfalt (Europe, the diversity) tells the story of Europe using more than 100 current satellite images to display the present and highlight the past incorporating political, economic and social challenges that are facing the continent. From historical sights of significance that have lasted the span of time to current villages and cities where urbanisation and migration are clearly visible, the satellite images portray Europe in many differing aspects.

“European Space Imaging is the market leader of very high resolution imagery across Europe. We are delighted to be able to exclusively partner with EOVision on this project and supply imagery showcasing the diversity of Europe” said Managing Director, Adrian Zevenbergen.

The satellite images included within the book also identify the changing landscapes, natural and man-made, to be found in Europe. Due to the long history of the continent and comparatively dense populations, hardly untouched landscapes are acknowledged, however predominantly the continent is used extensively. Furthermore topics such as border controls, refugee migration, global warming and climate change are discussed.

“It was very important for us to include only the best satellite imagery of Europe available within this book. This is why we chose to partner with European Space Imaging. Not only is the quality of their imagery outstanding, but their flexibility and personalised customer service made the entire process very easy” commented eoVision editor Gerald Mansberger.

The book will be available from Sunday 15 October 2017 and can be purchased at www.eovision.at

Share on facebook
Share on twitter
Share on linkedin

Related Stories

Interested In More?

Products & Solutions

Explore the world’s highest quality satellite imagery solutions

Explore

Industry Applications

Discover the wide range of innovative applications

Discover

Ask Questions

Our team of geospatial experts can guide you to your solution

Contact Us
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