The FIFA World Cup 2018 tournament will kick off in Russia tomorrow and to celebrate this great sporting event European Space Imaging have released satellite images showcasing each stadium from a birds-eye perspective.
The images were captured with DigitalGlobe satellites WorldView-3 and WorldView-4 at true 30 cm spatial resolution – the highest resolution currently commercially available. This allows football fans a unique view of where the 64 games that will decide this summer’s World Cup will be played.
The matches will be played across 12 stadiums, six of which were specifically built for the games costing almost €1.5 billion. It is expected that up to 1 million fans from all over the world will visit Russia for the tournament.
“No other satellite provider is able to provide imagery at true 30 cm resolution so it is very exciting to be able to share these detailed images of the World Cup Stadiums with football fans ” said Adrian Zevenbergen, European Space Imaging’s Managing Director.
Kazan Arena, Kazan | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Spartak Stadium, Moscow | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Nizhny Novgorod Stadium, Nizhny Novgorod | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Fisht Stadium, Sochi | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Saint Petersburg Stadium, St Petersburg | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Luzhniki Stadium, Moscow | WorldView-3 | © European Space Imaging
Mordovia Arena , Saransk | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Volgograd Stadium, Volgograd | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Rostov Arena, Rostov-on-Don | WorldView-3 | © DigitalGlobe – supplied by European Space Imaging
Kaliningrad Stadium, Kaliningrad | © DigitalGlobe – supplied by European Space Imaging
Samara Arena, Samara | © DigitalGlobe – supplied by European Space Imaging
Ekaterinburg Arena, Ekaterinburg | © DigitalGlobe – supplied by European Space Imaging
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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.
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.
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.
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.