Satellite Images Of Flooding In Livorno, Italy

Eight people have lost their lives in flooding caused by heavy rain on the 9th and 10th of September in the port city of Livorno.

Authorities issued an ‘orange’ alert before the flood, but a month’s worth of rainfall in just four hours led to much worse flooding than expected.

On the 13th of September European Space Imaging collected imagery using WorldView-2, a very high resolution satellite in the DigitalGlobe constellation. The images show muddy water sitting in industrial areas and parts of the countryside.

“Being able to assess the scale of flood damage using satellite data is very useful for emergency services and local authorities,” says Adrian Zevenbergen, Managing Director at European Space Imaging. “It can help them accurately assess the worst hit areas, allowing them to make decisions about where best to direct their resources.”

The flood comes after a summer in which Italy has been plagued by drought.

New satellite imagery obtained by European Space Imaging shows flooding in Livorno by WorldView-2 @ 50cm resolution

Satellite image of Livorno on 13th September by WorldView-2 @ 50cm resolution

Flooded fields by WorldView-2 @ 50cm resolution

The muddy river flowing into a turbulent Mediterranean by WorldView-2 @ 50cm resolution

Share on facebook
Share on twitter
Share on linkedin

Related Stories

Interested In More?

Products & Solutions

Explore the world’s highest quality satellite imagery solutions

Explore

Industry Applications

Discover the wide range of innovative applications

Discover

Ask Questions

Our team of geospatial experts can guide you to your solution

Contact Us
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