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Satellite Image Accuracy - What is geolocational accuracy and what does it mean for your data?

Satellite imagery has revolutionised industries, all around the world. The resolution and clarity of these images keeps increasing and because of that, it is important to understand the geolocational and geospatial accuracy of an image. This information is often detailed in a single line of text of a long table of technical specifications. However, this tiny bit of information can be critical to the success of many projects – for certain applications, using the clearest imagery available will be enough. However, for many others, knowing the geographic accuracy of each pixel, is going to be crucial. In this video, European Space Imaging dives into defining the different terms related to image accuracy, explains the different ways in which it’s calculated, and demonstrates why a geospatially accurate image is imperative.

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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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