september, 2023

27sep3:00 pm5:00 pmGEOSERIES - AERIAL VS 15 CM SATELLITE IMAGERY FOR LARGE-SCALE MAPPINGOnline

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Event Details

Do you need to map a large urban area? This webinar presents all you need to make a well-informed decision between aerial and 15 cm satellite imagery. Earth observation experts talk about:

  • the advantages and disadvantages of aerial and satellite imagery
  • the possibility to combine both to get the best of both worlds
  • differences in collection planning and data processing
  • Intelligent Collection Planning and Near Real-Time delivery
  • satellite and aerial imagery data processing workflows and the key factors that affect productions
  • the possibilities of reducing sun glint and other challenging operation settings

BONUS: Daniela Valentino from Planetek Italia presents a real-life user case from a European municipal mapping project.

Time

(Wednesday) 3:00 pm - 5:00 pm

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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.

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