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Satellite Imagery For Agriculture &Forestry Optimising Crops With Precision imagery Evaluate vegetation health Valuable multispectral bands allow in-depth analysis of plant and soil conditions…

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Industry Applications Geospatial information in the form of satellite imagery and its derived applications have wide reaching capabilites to optimise workflows and disrupt entire…

18 European Landmarks in Satellite Images

Satellite sensors captured unique architecture, breathtaking nature and centuries of history. Explore the Colloseum, La Sagrada Familia, the Leaning Tower of Pisa, and other…

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Imagery Basemaps The foundation for large scale mapping Free Samples Seamless Global Imagery Cost Efficient Pay only for your AOI at a competitive price…

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Satellite Imagery For Energy Enhancing energy infrastructure Manage Assets Monitor heavy equipment and RoW encroachment remotely Plan New Infrastructure Utilise advanced data to enhance…

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Satellite Imagery For Urban Planning Satellites for sustainable Growth Enforce Regulations Detect and confirm new structures and monitor environmental compliance Plan New Infrastructure Utilise…

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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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