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 3D map covering worldwide land spaces with 5 meter resolution plus 50 cm resolution on-demand. Available products:

  • AW3D Standard – DSM / DTM in 2.5 m or 5 m resolution
  • AW3D Enhanced – DSM / DTM in 0.5 m, 1 m or 2 m resolution.

These datasets will prove useful across all industry applications, especially energy and mining, defence and intelligence, civil government and engineering.

Additional Resources

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