JUFRA DISTRICT, LIBYA: GNA Destroys LNA Supply Aircraft
European Space Imaging has collected satellite images on July 29 depicting two destroyed aircraft at Al-Jufra Airbase.
July 26- Libya’s Government of National Accord (GNA) carried out airstrikes against Al-Jufra Airbase. The base is described as a “key staging post” for the opposing political faction, Libyan National Army (LNA).
At least one person, a pilot, was reportedly killed during the airstrike. The two Ilyushin Il-76 transport aircraft, belonged to Ukrainian Airline, Europe Air.
Overview of Al-Jufra Airbase | GeoEye-1 | 40 cm Resolution | Satellite Imagery © 2019 European Space Imaging
A close up image of the two destroyed Il-76 transport aircraft | GeoEye-1 | 40 cm Resolution | Satellite Imagery © 2019 European Space Imaging
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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.