Moria, Greece: Satellite Images Show Fire Damage

Nearly 13,000 people are left without shelter after the fires have burnt down most of the Moria Camp on the island of Lesbos, Greece.

Very High Resolution (VHR) satellite images captured yesterday with WorldView-3 by European Space Imaging showing the damage to Europe’s largest migrant camp. The fires started early Wednesday and destroyed almost all of the camp completely. The refugee camp is home to an estimated 13,000 people, more than six times its maximum capacity of 2,200 people.

The cause of the fire is still unknown, however it has been reported that the fires were started by Moria camp residents complaining about the coronavirus-related lockdown measures. The camp has been under lockdown after 35 people tested positive for Covid-19 earlier this week.

{
  bandList = 
  [
    3;
    2;
    1;
  ]
}{
  bandList = 
  [
    4;
    3;
    2;
  ]
}

Satellite images showing the refugee camp before and after the fire | Captured on 20 Aug. 2020 & 9 Sept. 2020 by WorldView-2 & WorldView-3 respectively | © European Space Imaging

Share on facebook
Share on twitter
Share on linkedin

Related Stories

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