2023 SAG Award Winners

Lantmäteriet (Land Survey of Sweden)

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Project Goal

Automatic generalization was part of a larger project with the task of modernizing Lantmäteriets cartographic products through production to finished products.
The content of the different map series has changed by harmonizing the information in the different scales regarding what is displayed at which scale, how data models are designed, and how the information is presented. Product formats and delivery systems were also modernized at the same time. All with the aim of making the information easier to use for our users.
One goal of the project was to reduce manual work through automation. It was decided that the production of the map series in the scale 1:50,000 and 1:100,000 would be done using automatic generalization.
The goal was that the generalization to 1:50,000 and 1:100,000 should be automatically without the need for post-processing or manual checks, a goal that was met by the project.
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Business Problem Solved

Lantmäteriet had different map series in scales 50’ and 100’ which were manually updated when a change occurred in our 10’ database.
Now when a change is identified in our 10’ database it triggers a new partition of 10x10 km being generated for 50’ and 100’ and being swapped in the databases. The process is fully automatic and match the new partition with the existing database in the partition border. The generalization is done in parallel on several servers.
This makes our map databases always up to date and the customers gets the latest map updates instantly. We also only have one source of data to be maintained.
In the project we have also modernized and harmonized data models for the different cartographic databases.

Technology Implemented

Our generalization process is built mainly with model builder in ArcGIS Pro and some parts with python code. We started the project working with model builder in ArcMap but decided to migrate the entire process to ArcGIS Pro.
We use ArcGIS Enterprise to monitor when our process is run at the server and view the finished result.
A dashboard is used to monitor the generalization at the server. Each partition is logged when the different steps of the generalization process start and control of the finished result is written to the dashboard so you can follow the process.
The finished result of the generalization is written to a ArcSDE-database and can be viewed immediately in an internal web app.

Development Team Biography

Mikael Johansson
Magnus Westberg
Fredrik Lundberg
Terese Domesjö
Linnéa Söderblom
Erik Sisell
Gabriel Bengtsson
Annika Fransén
Richard Nylander
Mikael Åhlberg
Marcus Öhman
Jessica Edberg
Yang Zhang
Mattias Frick