This plugin uses DEAP to implement a semantic segmentation backend plugin for Raster Vision. The plugin operates by using Genetic Programming to evolve raster algebra formulas to perform specific detection tasks.

You'll need docker (preferably version 18 or above) installed. After cloning this repo, to build the Docker images, run the following command:

> docker/build

Before running the container, set an environment variable to a local directory in which to store data.

> export RASTER_VISION_DATA_DIR="/path/to/data"

To run a Bash console in the Docker container, invoke:

> docker/run

This will mount the following local directories to directories inside the container:

• $RASTER_VISION_DATA_DIR -> /opt/data/
• genetic/ -> /opt/src/genetic/
• examples/ -> /opt/src/examples/

For debugging, it can be helpful to use a local copy of the Raster Vision source code rather than the version baked into the Docker image. To do this, you can set the RASTER_VISION_REPO environment variable to the location of the main repo on your local filesystem. If this is set, docker/run will mount $RASTER_VISION_REPO/rastervision to /opt/src/rastervision inside the container. You can then modify your local copy of Raster Vision in order to debug experiments running inside the container.

Using the plugin requires making a Raster Vision profile which points to the location of the plugin module. You can make such a profile by creating a file at ~/.rastervision/plugin containing something like the following.

[PLUGINS]
files=[]
modules=["genetic.semantic_segmentation_backend_config"]

To test the plugin, you can run an experiment using the SpaceNet Vegas Buildings dataset. A test run can be executed locally using something like the following. The -p plugin flag says to use the plugin profile created above.

export RASTER_VISION_REPO=/path/to/raster-vision
export RASTER_VISION_DATA_DIR=/path/to/SpaceNet_Buildings_Competition_Round2_Sample

export RAW_URI="/opt/data/AOI_2_Vegas_Train"
export PROCESSED_URI="/opt/data/genetic/vegas/processed-data"
export ROOT_URI="/opt/data/genetic/vegas/local-output"
rastervision -p plugin run local -e examples.semantic_segmentation.vegas_buildings -a raw_uri $RAW_URI -a processed_uri $PROCESSED_URI -a root_uri $ROOT_URI -a test True --splits 2

The train output folder will then contain a text file with the best-performing formula for calculating the segmentation task.