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GEOSeries Episode 2 - Satellite Imagery Saving Lives in Conflict Zones

From locating and disarming landmines to combating illegal mining, monitoring armed conflict, mapping internally displaced populations and subsequent camps as well as tracking oil pollution affecting local citizen’s health and the surrounding environment, satellite imagery can provide real-time insights that can help to save lives and increase citizen safety.

Providing timely and objective data at a variety of spatial and temporal scales, the technology is able to access inaccessible terrains to support humanitarian teams to monitor and verify conflict zones in order to support and aid complex emergencies. In terms of peace and security, VHR satellite imagery is being used in conflict areas to aid refugee relief operations, assess contravention in human rights, as evidence for verifying international laws and treaties violations and assessing environmental impacts exacerbated by conflict.

Join European Space Imaging in a thought-provoking discussion with panel members Josh Lyons, Human Rights Watch, Wendi Pedersen, Geneva International Centre for Humanitarian Demining (GICHD) and Wim Zwijnenburg, PAX for Peace covering all aspects of how satellite imagery saves lives in conflict zones.

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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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