Daily Revisits

More frequent collections means more precise data

Paris multi collection

Around the clock Imagery Collection

Actionable Intelligence

Multiple daily opportunities plus low latency makes intellegence more actionable

Precision Monitoring

The ability to monitor a single point on a sub-daily basis

Efficient Collections

Agility and frequency means large collection projects have higher success rates

European Space Imagery has access to three Very High Resolution (VHR) satellite constellations after the launch of Maxar’s WorldView Legion in 2022. This means users have unparalleled collection opportunities across Europe and North Africa.

The ability to capture a single Area of Interest (AOI) 5, 10 or even 20 times in a single day has significant impact on the completion of challenging collections involving bad weather or developing situations such as active missions or natural disasters.

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Tipping & Cueing

Tipping & Cueing services from European Space Imaging combines Synthetic Aperture Radar (SAR) and optical imagery to locate small targets within a large area. 

This valuable service benefits greatly from frequent satellite revisits. In an open ocean environment, “dark” vessels are constantly on the move and potentially avoiding detection. When a SAR image is used as a tip in order to cue a VHR image collection, time is of the essence. Even a single hour can mean the difference.

Emergency RESPONSE

Emergency situations change rapidly, but one thing remains constant – the need for accurate and timely intelligence. First responders rely on fresh imagery in order to map damages, plan evacuations and deliver supplies. 

As fires burn or flood waters rise, imagery from earlier in the day may no longer be accurate. The ability to collect a dedicated AOI multiple times per day gives rescuers and officials the tools they need to save lives and mitigate damage. 

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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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