Automated Generalization - examples

In all of the 1:10'000 and 1:25'000 examples here, German NAS data was imported into a German-specific AAA data model and automatically generalized. Text was placed automatically as a final step. In these examples, there was no manual intervention at any point. At this scale, building generalization, displacement and data thinning are especially important. Although the data and the model are specific to Germany, data in any model and scale can be automatically generalized.

Examples 1:10'000 scale: The generalization of four 10'000 scale map sheets with approx. 165'000 objects took a total of 3 hours and 45 minutes. 500 pieces of text were placed in 7 minutes.

Left: ungeneralized section of 1:10'000 scale Right: The same section of 1:10'000 scale automatically generalized. Below: with automatic placement of labels

Left: ungeneralized section of 1:10'000 scale Right: fully automatically generalized section of 1:10'000 scale Below: with automatic text placement

Examples 1:25'000 scale: The following are examples of a 1:25'000 scale map. At this scale, data thinning, building generalization (which is a combination of several different tasks) and displacement are critical. The conditions applied in this case are specific to a 1:25'000 scale map.

The generalization of one 25'000 scale map sheet with approx. 155,000 objects took a total of 3 hours and 10 minutes. 184 pieces of text were placed in 4 minutes.

Left: ungeneralized 25'000 Right: fully automatically generalized 25'000 Below: 25'000 with automatic text placement

The following are examples of a 300'000 scale map that has undergone model and cartographic generalization. Above, the ungeneralized instance, below the generalized instance. (Source: Project SysDab, Copyright swisstopo 2009)

ungeneralized