Automatic Generalisation

You can view a practical example of automatic generalisation, and the requirements that the process is working to, by clicking on the link below.

The source data (captured at 1:25,000) has already undergone Model Generalisation to reduce the amount of data to a level suitable for representing at the target scale (1:50,000). This was a fully automated process that was achieved through the deletion, amalgamation and filtering of features. 

Once you have opened the link, using the blue cross on the right hand side of the image, you can toggle between the Source Data and the Target Data.  Toggling on the target data will show the preliminary automatic Cartographic Generalisation results achieved with Radius Clarity. This has had no manual intervention during either the Model or the Cartographic process and those conflicts that are not resolved automatically will then be marked up for manual finishing.

By clicking on each of the ‘pins’ in the map you can view a brief description of the requirement that is being met at that position.

View automatic generalisation example

Data courtesy of AdV (Landesamt für Vermessung und Geobasisinformation Rheinland-Pfalz)

Radius Clarity™ is 1Spatial’s platform for automated generalisation. It provides the facilities to build automated generalisation production flowlines and an environment to develop and research new generalisation algorithms.

The tool set in Radius Clarity enables small-scale digital data to be automatically derived from large-scale source data. Its approach to generalisation is based on intelligent software Agents, a technology developed by the ESPRIT project number 24939.

It employs agent technology; in a breakthrough unique to Radius Clarity, advanced artificial intelligence techniques have been introduced into the map production process. The agents enable the automated map production process to capture context sensitivity during processing.

Usually a human cartographer presented with conflicts uses skill and subjectivity to compromise between the appearance of features and produce a satisfactory solution; Radius Clarity applies equivalent intelligence to reach an equivalent solution automatically, whilst maintaining consistently high data quality.

Radius Clarity’s unique technology offers users the opportunity to derive new mapping products from existing data, to speed up the cartographic generalisation process and to reduce manual errors and inconsistencies within products.

Radius Clarity enables you to:

• Be more competitive by delivering the most up-to-date products - map products can be updated more frequently through automation
• Reduce maintenance costs and effort by reducing the number of scale dependent databases that need to be individually maintained
• Guarantee that data at different scales is always synchronised – many products can be updated by modifying a single database
• Improve customer satisfaction by ensuring that products are up to date and consistent across all scales.
• Open up new sales by introducing new products to suit market needs - automation allows you to deliver more products without the need for lots of additional manual effort.
• Produce consistent results through rules enforcement - automated generalisation reduces the inconsistent results that can occur with manual generalisation.

Radius Clarity documentation:

• Product Brochure
• Cartographic Generalisation
  Oldenburg, April 2008 (powerpoint)
• Cartographic Generalisation
  Oldenburg, April 2008 (powerpoint)
• Cartographic Generalisation
  Oldenburg, April 2008 (powerpoint)
• Cartographic Generalisation
  Oldenburg, April 2008 (powerpoint)

For more information, contact us

*Clarity emerged from an ESPRIT project (Ref #24939), is based on Agent technologies and was developed in conjunction with leading European mapping agencies.

Help set the direction of future generalisation projects by taking part in our generalisation questionnaire.

Key Features

• Goal Driven: Goals are set to specify the desired result and the generalisation system works towards that result, acting autonomously to achieve the goal
• Context Sensitive: Different generalisation algorithms will be automatically applied depending on the feature’s context to its neighbouring map features
• Self-Optimising: Radius Clarity has the ability to cycle through possible outcomes, adapting the initial geometries repeatedly until the optimal result is obtained
• Object-Orientated: The object-orientated data modelling means that map features (e.g. houses, roads, rivers) become active objects, providing measures, actions and constraints for automated generalisation
• Adaptive: The constraints and algorithms are easily adapted between different feature types and different scale changes
• Automatic: Includes a framework for running automated generalisation processes and a mechanism to instigate generalisation as a batch process
• Configurable: Complete user control of the data model, the target map specification and the generalisation approach

Benefits

• Reduces maintenance costs as only one source of data needs to be kept up-to-date
• Saves time and money by limiting manual checking and processing
• Quickly derives new products from existing data, reducing the time to market and opening up new sales opportunities
• Increases the frequency of product delivery into the supply chain; essential given the speed of real world change
• Addresses the reduction in cartographic skill by freeing cartographers to work on mission-critical tasks
• Produces consistent and reliable results that meet customer’s expectations, increasing confidence in, and the reputation of, your organisation
• Produces a documented set of known rules for the manufacture of small-scale products from large-scale products, saving time in recording internal processes

New features of this version: (v4.2)

• ‘Cookbook’ user guide, tutorial and reference guide
• Sample data for ease of learning and default example settings
• Enhancements to the agent core
• Enhancements to bundled algorithms
• Improved installation guide
• Data model design template

Platforms:

• Microsoft Windows XP
• Linux (SuSe 10.0)

Skills required:

• A basic understanding of object orientation
• A good understanding of geospatial data modelling
• A basic understanding of XML (for customising Radius Clarity)
• Advanced customisation requires Java knowledge

Java version:

• 1.4.2

Deployment options:

Single machine or Client / Server architecture. 1Spatial’s Data Access Manager (DAM) is used to support the Client / Server architecture, but can also be installed locally.

Licensing:

Single machine licences available as well as Client / Server architecture licences.

Supported scales:

With parameterisation any scale change can be supported.

• Datasheet