GEOSeries

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wHAT IS GEOSERIES?

GEOSeries is a virtual event program created by European Space Imaging to explore topics related to remote sensing and Earth Observation. It brings together professionals from all aspects of the geospatial industry as well as those working within the vertical applications. The aim of the program is not only to educate and inspire the industry, but to also spread the word of how satellite imagery and other geospatial products are being used to make the world a better place. 
 

The Next Episode | 7 October

Leveraging Artificial Intelligence To Create Global HD Vector Maps

Ecopia Global Feature Extraction (GFX) Powered by European Space Imaging is a unique partnership that utilises the freshest, highest quality satellite imagery along with the most advanced artificial intelligence from Ecopia.AI (Ecopia) to offer accurate geospatial feature extraction at continent-wide scale. It takes away the challenge of obtaining up to date VHR imagery and manually creating polygons by hand – both of which are labour intensive and can be quite costly. With 13 core features eligible for extraction, users receive comprehensive, up-to-date and accurate land cover maps with zero in-house GIS work and with automation-level speed and efficiency.

In this GEOSeries episode, Ecopia and European Space Imaging will discuss the many benefits of GFX and the solutions it provides. From land-cover capabilities for maintaining smart city digital twins, impervious surface mapping for water drainage assessment, detecting and analysing urban sprawl and flood risk modelling, and the software’s newest application – solar panel identification to aid tax rebates and provide accurate assessments for insurance purposes. Highlighting real use cases to demonstrate the power of these datasets, we will dive deeper into the software and how both government and commercial customers can benefit from this advanced product.

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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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