october, 2022

27oct3:30 pm5:00 pmGEOSeries Episode 2 - VIVID IMAGERY BASEMAPSOnline

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Vivid offers a suite of off-the-shelf imagery basemaps with up to 15 cm resolution and 2 m CE90 spatial accuracy. Vivid products – Basic, Standard, Advanced and Premium – are produced annually with the most current, clear imagery available. With high-quality image layers available immediately around the globe, Vivid is ideal for visualization, large area feature extraction, and providing context in maps and applications. This webinar will showcase the different Vivid offerings so that you can have confidence in your foundational imagery basemap and learn how best to incorporate it into your app or workflow.

 

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(Thursday) 3:30 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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