Maps almost always have lots of large and often complex polygons showing lakes, forests, urban areas, countries and so on. On a small scale map all you are going to see of some of these polygons is a few colored pixels. For that you don’t need the detailed data.

I want to write a bit about the processing architecture of osm2pgsql and how I plan to extend it to help with generalization.

In cartography you often have to find points that “represent” a polygon in some way, the exact outline of the polygon isn’t important, you just need a single position somewhere on that polygon, for instance to draw an icon there. In OpenStreetMap points of interest are sometimes modelled as polygons (usually building outlines) for example. A similar problem is finding a labelling point of a polygon, that is the point where to put the name label of a forest or a lake or a country.

To start the OSM generalization project I decided to look at some specific generalization problems with a wide variety of use cases and challenges, and with different types of solutions. It is obvious that I can’t implement every algorithm out there and solve every generalization problem, but I can take a sample of typical problems with different solutions and try to implement them. This should hopefully give us a better understanding of the problem space and a basis on which we can build later.

OpenStreetMap data is often incredibly detailed and the generated maps look amazing. But there is a problem for maps in smaller zoom levels/on smaller scales: There is often too much detail. Maps become slow to render and cluttered, the important information is hard to see. To solve this the map data needs to be generalized.