A flood west of Athens, Greece, was caused by heavy rain on November 15-16, and has killed 22 people.
The cities of Mandra, Elefsina, and Nea Peramos were most affected with 1,184 buildings suffering damage requiring significant government compensation.
European Space Imaging captured 30 cm resolution images of the area using the WorldView-4 satellite on November 21. The images clearly show the damage wrought by the torrents of water and mud, and the path the water took as it flowed down the sides of the mountains and into gullies.
“The satellite images were made immediately available to the Space Applications and Remote Sensing Institute of National Observatory of Athens for the purpose of planning and maintaining situational awareness of the event in collaboration with first responders and government agencies,” said Vana Giavi, Managing Director of TotalView, European Space Imaging’s partner in Greece.
It has been reported that inappropriate urban development may have been a major contributing factor to the event’s severity, as natural floodways have been blocked by unlicensed construction.
“The very high resolution satellite imagery will be an invaluable tool for the Greek government to detect the presence of illegal buildings, and to plan future flood-prevention infrastructure,” said Adrian Zevenbergen, Managing Director of European Space Imaging.
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.