Today the DigitalGlobe WorldView-3 satellite was launched from Vandenberg Air Force Base in California.
The satellite was sent up on a United Launch Allliance Atlas 5 rocket. The birth of this new satellite was imaged by its older sibling satellite WorldView-1 as you can see in this film.
WorldView-3 opens up a new era for the commercial optical satellite business having the highest resolution available to date across 8 multispectral bands and shortwave infrared bands.
European Space Imaging hopes to have imagery products available from this new satellite in the very near future.
Three land-surveying authorities finished large-scale mapping projects using very high resolution satellite images in 2024. These are the challenges, solutions and results:
Redefining low latency data, European Space Imaging (EUSI) offers Near Real-Time (NRT) satellite image delivery in only 15 minutes after collection. The new EUSI DAF
Redefining low latency data, European Space Imaging (EUSI) offers Near Real-Time (NRT) satellite image delivery in only 15 minutes after collection. The new EUSI DAF
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.