Aerial image segmentation

What we are going to do

In this tutorial, we will apply a tiling service and a segmentation service to an aerial image. The segmentation service is based on segment-geospatial, which in turn uses Facebook’s Segment Anything AI model. Both services are provided as pre-built Docker images on GitHub.

Semantic segmentation is the process of assigning an object class (e.g. tree, building, or street) to each pixel of an image. Our workflow creates segmentation masks, which are images where the detected classes are represented by different colors.

The workflow consists of two steps:

Split input image into tiles

The tiling service splits the input image into four smaller tiles.

Apply image segmentation

The segmentation service is applied in a for-each action to each image tile to create the segmentation masks.

The actions in the second workflow step are intended to run in parallel. If you’ve just installed Steep, you will most likely want to enable parallelization.

Why tile the image?

You might wonder why the input image needs to be tiled and why we don’t apply the segmentation to it directly. There are three reasons for that:

1. Quality. Aerial images typically have a high resolution (e.g. 5000x5000 pixels, like the one below), and AI models tend to perform badly on large images. Consider the following pictures where we applied the segmentation on an input image directly and then executed the workflow with the same image but with 2×2 tiles and 5×5 tiles. The smaller the tiles, the more precise the results are and the more objects the model is able to identify.

No tiling

2×2 tiles

5×5 tiles

2. Lower memory consumption. The smaller the tiles are, the less memory each segmentation process needs. This is particularly important if you enable parallelization and run multiple processes on the same machine. Also, if you enable GPU acceleration, depending on your hardware, your graphics card memory might not be large enough to process a high-resolution image and the segmentation service will crash.

3. For the sake of demonstration. With this tutorial, we aim to teach you how to use a for-each action to process the results of a service with another service in parallel.

Image tiling will result in a higher quality, but it also has an impact on performance. The more tiles you create, the longer the workflow will take.

Step 1: Download input data

The most important thing for any data processing workflow is the data. In this tutorial, we use an aerial image provided by the Hessische Verwaltung für Bodenmanagement und Geoinformation in Germany through https://gds.hessen.de. The image is free and can be used for any purpose.

Use the following link to download the full resolution image:

https://raw.githubusercontent.com/steep-wms/aerial-image-segmentation/1.0.0/data/dop20_32_475_5524_1_he.jpg

In the following, we assume that the image has been saved to:

/data/dop20_32_475_5524_1_he.jpg

If you’ve downloaded the image to another location on your computer, make sure you modify the paths in the subsequent steps.

Step 2: Add service metadata

Similar to the previous tutorials, we need to add metadata for the tiling service and the segmentation service. Open the file conf/services/services.yaml and add the following code to it to describe the tiling service:

- id: tile
  name: Tiling
  description: Split an image into tiles
  path: ghcr.io/steep-wms/aerial-image-segmentation/tiling-service:1.0.0
  runtime: docker
 
  parameters:
    - id: input_file
      name: Input image
      description: The input image to tile
      type: input
      cardinality: 1..1
      dataType: string
 
    - id: output_directory
      name: Output directory
      description: The directory where the tiles should be stored
      type: output
      cardinality: 1..1
      dataType: directory
 
    - id: num_tiles
      name: Number of columns/rows
      description: >-
        The number of columns and rows to split the image into. A value of
        5 means the image will be split into 5x5 tiles.
      type: input
      cardinality: 1..1
      dataType: string

The service has three parameters: one for the input image file, one for the output directory where the tiles will be stored, and one specifying the number of columns and rows to split the image into. Regarding the latter, a value of 5 means the image will be split into 5×5 (=25) tiles.

As the service is provided as a pre-compiled Docker image on GitHub, we set the attribute path to the Docker image name and the attribute runtime to docker.

Now, add the metadata for the segmentation service:

- id: segment
  name: Image segmentation
  description: Segment geospatial data with the Segment Anything Model (SAM)
  path: ghcr.io/steep-wms/aerial-image-segmentation/segmentation-service:1.0.0
  runtime: docker
 
  parameters:
    - id: input_file
      name: Input image
      description: The input image to segment
      type: input
      cardinality: 1..1
      dataType: string
 
    - id: output_file
      name: Output image
      description: Output image with masks
      type: output
      cardinality: 1..1
      dataType: string
      fileSuffix: .jpg

The service has just two parameters: one for the image to segment and one for the filename of the output segmentation mask. We set the fileSuffix of the output parameter to .jpg to make sure the filename will be generated with the right extension.

Again, we use the Docker image name as path and set the runtime to docker.

Step 3: Configure Steep

Our two services will be executed in Docker containers, which have a virtualized file system. Any data location on the host system that should be accessible from within a Docker container needs to be mounted when the container is started.

In our case, this applies to the /data directory where the input image has been downloaded. We will use the same directory for Steep’s temporary path as well as its output path to access the segmentation masks after the workflow has finished.

steep:
  tmpPath: /data/tmp
  outPath: /data/out

steep:
  runtimes:
    docker:
      volumes:
        - /data:/data

Step 4: Create the workflow

Create a new file segment.yaml and paste the following workflow into it:

api: 4.7.0
actions:
  - type: execute
    service: tile
    inputs:
      - id: input_file
        value: /data/dop20_32_475_5524_1_he.jpg
      - id: num_tiles
        value: 2
    outputs:
      - id: output_directory
        var: tiles
 
  - type: for
    enumerator: i
    input: tiles
    actions:
      - type: execute
        service: segment
        inputs:
          - id: input_file
            var: i
        outputs:
          - id: output_file
            var: mask_image
            store: true

The workflow first applies the tiling service tile to the input image. The service writes its results into a directory. The segmentation service segment is then applied in a for-each action to each image tile in this directory. Steep will create a new process chain for each image tile and execute them in parallel if possible.

Step 5: Submit the workflow

Run the following command to submit the segmentation workflow to Steep:

curl -X POST http://localhost:8080/workflows --data-binary @segment.yaml

You can monitor the workflow execution in Steep’s web UI. Note that it will take several minutes to complete.

Also, if you haven’t done so already, the Docker images need to be pulled first. They have a total download size of about 8 GB, so depending on your Internet connection, downloading also may take a few minutes. Subsequent runs will be faster.

After the execution has finished, you will find the segmentation masks at /data/out/[WORKFLOW ID].

Optional: Enable GPU acceleration

If your machine has an NVIDIA graphics card, you can optionally enable GPU acceleration to tremendously speed up processing. In fact, this is recommended for any AI workflow in production.

To do so, the NVIDIA Container Toolkit needs to be installed on your system. Follow the installation guide if you haven’t done so already.

Modify the metadata of the segmentation service and add the following runtime argument:

- id: segment
  name: Image segmentation
  
  ...
 
  runtimeArgs:
    - id: gpus
      name: GPUs
      description: "Use all available GPUs"
      label: "--gpus"
      value: "all"

This will tell Steep to pass the argument --gpus all to the docker run command when it starts the segmentation service.