may, 2024

02may3:00 pm4:30 pmGEOSERIES - MGP Pro: The Next Generation of SecureWatch for On-demand Access to VHR ImageryOnline

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Event Details

EUSI is joined by Maxar to discuss the exciting upgrade from SecureWatch to MGP Pro. As part of the new Maxar Geospatial Platform, MGP Pro gives customers on-demand access to the world’s most recent high-accuracy, high-resolution satellite imagery and analytics. MGP Pro is the premier, cloud-based subscription platform for secure and timely access to Earth Intelligence. With a broad range of imagery and geospatial data products, MGP Pro provides unrivalled coverage, quality and flexibility. MGP Pro subscribers can access over 3 million square kilometers of daily image collections, plus more than 6 billion sq km of archived imagery at up to 30 cm resolution.

With new features and new subscription models, there is plenty to learn in this exclusive webinar session aimed at potential high-level users and distributors of MGP Pro.

Time

(Thursday) 3:00 pm - 4:30 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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