Ecopia Global Feature Extraction (GFX) Powered by European Space Imaging

European Space Imaging has recently signed a contract with Ecopia.AI, a trusted partner and global innovator in HD Vector Maps harnessing artificial intelligence and innovative cloud computing, to bring Ecopia Global Feature Extraction (GFX) Powered by European Space Imaging to customers worldwide.

This unique partnership between Ecopia.AI and European Space Imaging delivers an unparalleled competitive advantage of scale and accuracy. Incorporating fresh very high resolution satellite imagery collected by European Space Imaging, Ecopia leverage’s artificial intelligence based systems to rapidly extract vector maps at a continental-scale. This drastically reduces human effort and can generate and maintain millions of km2 of VHR Vector Maps for any area on the earth, all with the quality of a trained GIS professional.

“We are delighted to be able to service customers worldwide with this innovative product, offering an alternative to historically outdated vector map sources,” said Adrian Zevenbergen, Managing Director of European Space Imaging. “It is an exciting product that offers extensive time and cost savings to our customers and will allow us to significantly expand our geographic business operations”.

However, GFX offers much more than just building footprints. This powerful product can also identify land-based features from a highly-accurate, scalable, 12 class solution that provides contextual information regarding transportation networks. It performs the work of a dedicated GIS specialist in a fraction of the time, allowing your team to jump right into analyzing the data, thus making the product highly versatile and profitable across a broad spectrum of applications.

“Ecopia engages with market-leading data providers to form an ecosystem of partners that can empower the best solutions for our respective clients,” said Jon Lipinski, Co-Founder and President of Ecopia. “We are excited to be partnering with European Space Imaging as part of our effort to expand this global ecosystem.”

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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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