Satellite Imagery For

Civil
Government

urban planning

See The Best Way Forward

Automated Mapping

VHR imagery combined with Artificial Intelligence reduces the need for GIS specialists

Enforce Regulations

Detect and confirm new structures and monitor environmental compliance

Grow Sustainably

Utilise advanced datasets for safety, innovation and sustainability

Very High Resolution (VHR) satellite imagery gives you a new perspective from which to make informed decisions about the security and future of your city or state. Monitor the change in your environments, accurately map in very fine detail, and plan for the future with the best data available.

A methodology to classify urban poor areas enabling improved infrastructure

Read about this and more satellite imagery applications in the Urban Planning INCITE industry report: From Urban To Rural – Enabling Sustainable Urban Planning and Development Using Satellite Imagery

SMART DECISION-MAKING

The best decisions are informed ones. Very high resolution satellite images show you the situation as it really is in near real-time, optimising your response to any situation. Whether you are seeking new information about an event, or want to confirm other sources of information, satellite imagery can help you answer questions about:

  • Migration
  • Urban planning / Sustainable development
  • Asset management
  • Tax control
  • Roads and utilities management
  • Construction
  • Emergency planning

MONITOR CHANGE

To truly understand the past it is useful to have a record of it. Our world is being imaged more frequently than ever before, giving us an image library documenting the changes that have taken place over recent decades. We are able to supply this archive imagery, and can also arrange new satellite image acquisitions over your area of interest – so you can see what is happening there now. This can support operations such as:

  • Agricultural controls
  • Detecting illegal buildings
  • Maintaining cadastral maps
  • Monitoring regulatory compliance

Learn More About

Ecopia GFX
By European Space Imaging

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.

Barcelona Layers gradient

Learn More About

Download the Latest Edition of INCITE

From Urban To Rural: Enabling Sustainable Urban Planning and Development Using Satellite Imagery

The definitive guide to acquiring and using satellite imagery in for civil government officials.

Discover
What's

NEW

Scroll to Top

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.

X