RapidDEM

The freshest VHR elevation data with urgent delivery so you can get to work immediately

government

The Most Up-to-date Elevation Data

Surface & Terrain

Accurate elevation models containing both DTM and DSM data

Next Day Delivery

Digital Elevation Models are delivered in as little as 24 hours after image collection

30 cm Resolution Textures

The highest quality satellite imagery used as textures on all sides

European Space Imaging has partnered with GAF AG to offer this revolutionary elevation product. With RapidDEM, users can get fresh Very High Resolution (VHR) Digital Elevation Models (DEM) delivered 24 hours after collection.

Utilizing a sophisticated method whereby five VHR stereoscopic images are collected from varying angles on a single satellite pass, European Space Imaging is able to provide incredibly accurate elevation data and the highest quality 30 cm resolution textures to GAF AG, who then builds the 3D model and rush delivers it to the end user.

The ability to deliver up-to-date elevation models has high impact applications for:

  • GEOINT – Fresh 3D models of critical areas for mission planning and rapid decision making
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  • Construction / Infrastructure Planning – When elevation data is old or missing during the site selection process
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  • Emergency Management – Post-event 3D models for landslides, earthquakes or mountainous terrain delivered in time for response crews to utilise
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See how our imagery fits your project

Download imagery, mapping and 3D product samples.

Demo: Lisbon, Portugal textured model

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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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