Satellite Imagery For

Urban
Planning

government

Satellites for sustainable Growth

Enforce Regulations

Detect and confirm new structures and monitor environmental compliance

Plan New Infrastructure

Utilise advanced data to enhance the site selection process 

Analyse Impact


Assess vegetation health, ground water and human activity changes

Very High Resolution satellite images offer a unique view of what lays in, on and around urban and rural settlements. It provides a cost-effective and simple method of monitoring wide areas both locally and globally, as opposed to using in situ data, and is an indispensable tool for managing the actions and events that impact urbanisation leading to urban sprawl.

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

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 the urban planning industry.

DETAILED ANALYSIS

Very high resolution satellite imagery is clear enough to show road lines, sidewalks, vehicles, small structures and even people. This allows for precise analytics for city development. It is ideal for:

  • Studying Traffic Patterns
  • Crowd Management
  • Small Feature Identification
  • Verifying Property Lines
  • Mapping Road Features
  • Walkability Analysis

MATERIAL IDENTIFICATION

We offer more spectral diversity and better spatial resolution than any other satellite imagery provider – allowing you to see more of what is actually happening in your city. Multispectral imagery can assist in identifying building materials, separating organic and synthetic surfaces, analysing plant health and much more.

This has endless applications including environmental impact studies, compliance reporting and impervious surface calculations.

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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

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City Morphology

Large-scale monitoring with VHR satellite imagery provides valuable information about the morphology of a city. Understanding the patterns of new construction and traffic in a rapidly growing municipality provides insights into new infrastructure requirements.

Mapping and GIS activities are greatly enhanced with the use of VHR multispectral and stereo satellite imagery.

City Morphology graphic

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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 the urban planning industry.

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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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