flood-mapper

A GBDX task that maps flood water on 4-band or 8-band atmospherically compensated DigitalGlobe imagery.

The algorithm employs a pixel-based, generic material discriminator that produces an estimate for each pixel being member of a water-saturated body.

The task produces a mask where white corresponds to flood water and black to the background (see example below).

Run

In a Python terminal:

import gbdxtools

gbdx = gbdxtools.Interface()

fm = gbdx.Task('flood-mapper')
fm.inputs.image = 's3://gbd-customer-data/32cbab7a-4307-40c8-bb31-e2de32f940c2/platform-stories/flood-water/north-carolina/10400100238BDE00/ms'

# Run workflow and save results
wf = gbdx.Workflow([fm])
wf.savedata(fm.outputs.image, 'platform-stories/trial-runs/flood-water')
wf.execute()

Input ports

Name	Type	Description	Required
image	directory	Contains input image. The input image must be a 4/8-band atmospherically-compensated multispectral image. If more than one images are contained in this directory, one is picked arbitrarily.	True
tolerance	string	A percentage value. High tolerance picks up everything with a detectable concentration of moisture or surface water. Intermediate tolerance picks up water-saturated soils and other materials. Low tolerance picks up only impure water bodies. Default is 50.	False
min_size	string	Minimum target area in m2. Default is 1000.	False
min_width	string	Minimum target width in m2. Default is 10.	False
confidence	string	Minimum confidence to determine flood water pixel. Default is 15.	False

Output ports

Name	Type	Description
image	directory	Contains output mask.

Development

Build the Docker image

You need to install Docker.

Clone the repository:

git clone https://github.com/platformstories/flood-mapper

Then:

cd flood-mapper
docker build --build-arg PROTOUSER=<GitHub username> \
             --build-arg PROTOPASSWORD=<GitHub password> \
             -t flood-mapper .

Try out locally

Create a container in interactive mode and mount the sample input under /mnt/work/input/:

docker run -v full/path/to/sample-input:/mnt/work/input -it flood-mapper

Then, within the container:

python /flood-mapper.py

Confirm that the output image is under /mnt/work/output/image.

Docker Hub

docker login

Tag your image using your username and push it to DockerHub:

docker tag flood-mapper yourusername/flood-mapper
docker push yourusername/flood-mapper

The image name should be the same as the image name under containerDescriptors in flood-mapper.json.

Alternatively, you can link this repository to a Docker automated build. Every time you push a change to the repository, the Docker image gets automatically updated.

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