True 30 cm
VHR Imagery

The highest quality resolution imagery for projects that require unparalleled clarity.

Madrid Airport 30cm

The power of 30 cm

10 m

Suitable for large land area coverage but will not provide any detailed data.

1 m

Provides some level of detail but will hinder detection and measurement objectives.

30 cm

The highest detail available and ideal for object identification and mapping.

The power of 30cm Very High Resolution (VHR) satellite imagery lies in its ability to detect small objects. It allows you to achieve a level accuracy that’s necessary in order for your project to succeed. Through use of the WorldView-3 satellite and our ground control station near Munich, Germany, we are able to proficiently deliver 30 cm and other VHR imagery to you.

See how our imagery fits your project

Download imagery, mapping and 3D product samples.

capturing the truth

Other providers re-sample to achieve the appearance of Very High Resolution, but European Space Imaging captures in TRUE 30 cm resolution.

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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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