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Expanding Product Offering: AW3D

European Space Imaging is expanding our elevation product offering with the addition of AW3D, a highly accurate set of Digital Elevation Models with global coverage. AW3D uses millions of satellite imagery to create comprehensive high resolution global elevation models. It is considered to be the world’s first and most precise

FIFA World Cup 2018 Stadiums As Seen From Space

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

First Images From WorldView-3

On August 13, 2014, DigitalGlobe launched WorldView-3 into orbit. On August 19, a mere six days after launch, they completed commissioning the satellite bus and opened the door on the main telescope to begin imagery testing on the entire suite of WorldView-3’s 27 super-spectral bands. We are pleased to share

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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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