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 several WorldView-3 image examples from the first image collection taken with the new satellite over Madrid, Spain, to show our customers the potential of this new satellite. Because of the regulatory restrictions, the images can’t yet display the 30 cm native resolution data, so we’re sharing imagery resampled to 40 cm below.
Star forts are not only perfect examples of effective military engineering; they are also beautiful. Let’s join us on a fascinating journey through architecture and history – from space. All satellite images in this article were collected by WorldView-3, WorldView-2 or GeoEye-1 at 30–50 cm resolution.
Munich, Germany, Los Angeles, California, and Bangalore, India – European Space Imaging (EUSI), a leading provider of Very High Resolution (VHR) satellite imagery and Pixxel,
Automated collection planning systems come with limitations. That’s why in European Space Imaging (EUSI), we combine autonomous planning software with expert human interventions to achieve
This high quality 30 cm resolution Digital Surface Model (DSM) was created by GAF AG utilizing 30 cm Very High Resolution Tri-Stereo Satellite Imagery collected by European Space Imaging (EUSI). This model is part of EUSI’s initiative to map all major European metro areas in 3D on a yearly basis.
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.
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.
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.
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.