Ecopia GFX
Powered By European Space Imaging

Combining the world’s clearest imagery with the most advanced artificial intelligence to produce vector maps at scale.

Barcelona Layers gradient

high definition Vector maps at scale

SCALABILITY

Millions of square kilometres can be vectorised every month with advanced machine learning

ACCURATE IDENTIFICATION

Iterative AI-based systems extract vector maps at scale with the accuracy of a trained GIS professional

UP-TO-DATE

Leveraging the +3 million km2 of imagery collected daily by the WorldView Constellation

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. The product comes with up to 12 core features eligible for extraction, so users can receive comprehensive land cover maps with zero in-house GIS work. The resulting vector maps are delivered as easy-to-use shapefiles, enabling users to focus on necessary analytics rather than time consuming map making.

Want to see how it works?

See this powerful mapping tool in action with our interactive demo.

AUTONOMOUS DRIVING

As the task of driving becomes more automated, the role of digital maps shifts significantly. These next generation maps used for machines come in the form of a highly accurate and realistic representation of the road, generally referred to as high-definition (HD) maps.

Map layers created using Ecopia GFX Powered by European Space Imaging offer an accurate representation of roads and give users the ability to study lane model and lane geometry which are crucial attributes in autonomous vehicle navigation systems.

Ecopia Road mapping

MUNICIPAL GOVERNMENTS

As urban centres grow, municipalities and governmental agencies must work harder than ever to monitor current environmental conditions and changes across geographies over time. Without scalable methods of mapping the natural and man-made worlds, these entities find it difficult to adapt to ever-changing circumstances and efficiently plan green infrastructure improvements.

Vector maps produced using Ecopia GFX Powered by European Space Imaging are used for a wide range of processes, planning activities, and geospatial analyses, including:

  • Property tax assessment
  • Planning civil engineering projects
  • Road & pavement management
  • Flood mapping
  • Land use analysis
  • Urban sprawl planning
UK Ecopia roads and buildings
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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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