september, 2024

12sep3:00 pm4:00 pmGEOSERIES - The Launch of Maxar’s Legion Satellites & The Impact On European EO ApplicationsOnline

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Event Details

The first two long awaited Legion Satellites are now orbiting Earth. What does this mean for space-based remote sensing projects around Europe?

In this webinar, EUSI is joined by representatives from Maxar Technologies and key European users of Very High Resolution (VHR) satellite imagery. We will discuss the unique technology within these satellites and how this significent increase in capacity of 8-band multispectral 30 cm class imagery is already poised to impact ongoing projects and increased demand across all sectors including: Large Area Mapping, Security, Emergency Response, Agriculture and Reasearch/Education.

In this webinar you’ll:

– See some of the first images collected by the Legion Satellites

– Get updates on the staus of the next four awaiting launch

– Experience simulations of the entire Maxar Constellation operating at full capcity over crucial latitutes

– Witness the advantages of 8-band imagery compared to 4 bands

– Hear testimonials from actual stakeholders in the European Earth Observation industry

Time

(Thursday) 3:00 pm - 4:00 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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