Wang–Müller line generalization algorithm in PostGIS

This is Wang–Müller line generalization algorithm implementation in PostGIS. Following "Line generalization based on analysis of shape characteristics" by the same authors, 1998.

Status & Structure

There are 2 main pieces:

• wm.sql, the implementation.
• MSc thesis mj-msc-full.pdf with visual examples and known issues.

It contains a few supporting files, notably:

• tests.sql synthetic unit tests.
• test-rivers.sql tests with real rivers.
• Makefile glues everything together.
• layer2img.py converts a PostGIS layer to an embeddable image.
• aggregate-rivers.sql combines multiple river objects (linestrings or multilinestrings) to a single one.
• init.sql initializes PostGIS database for running the tests.
• rivers-*.sql are national dataset snapshots of rivers (Makefile contains code to update them).
• ... and a few more files necessary to build the paper.

Running

make help lists the select commands for humans. As of writing:

# make help
clean              Clean the current working directory
clean-tables       Remove tables created during unit or rivers tests
help               Print this help message
mj-msc-full.pdf    Thesis for publishing
mj-msc-gray.pdf    Gray version, to inspect monochrome output
refresh-rivers     Refresh river data from national datasets
test-rivers        Rivers tests (slow)
test               Unit tests (fast)

To execute the algorithm, run:

• make test for tests with synthetic data.
• make test-rivers for tests with real rivers. You may adjust the rivers and data source (e.g. use a different country instead of Lithuania) by changing the Makefile and the test files. Left as an exercise for the reader.

N.B. the make test-rivers fails (see test-rivers.sql), because with higher dhalfcircle values, the unionized river (salvis) is going on top of itself, making the resulting geometry invalid during the process.

Building the paper (pdf)

# make -j mj-msc-full.pdf

mj-msc.tex results in mj-msc-full.pdf. This step needs quite a few or a container: see Dockerfile for dependencies or in-container to run it all in the container.

Contributing

This repository does not accept contributoins. Please fork it. If a fork has improved the algorithm substantially, you are welcome to ping me, I will link to it in this README.

Credit

Nacionalinė Žemės Tarnyba for the river data sets.

License

GPLv2 or later.