Raster annotation is a process used to assign a category to each pixel in an image. This technique is commonly employed in various fields, such as computer vision, remote sensing, and medical imaging, to create precise and structured datasets for training machine learning models.

What Is Raster Annotation?

Raster annotation involves labeling each pixel in an image with a specific category. Initially, all pixels are assigned a “no label” category. The annotation process consists of systematically assigning categories to the pixels corresponding to different objects or regions in the image. The result is a labeled mask that represents the categorization of every pixel in the image.

Tools for Raster Annotation

To perform raster annotation effectively, we use several tools:

• Paintbrush: Allows manual pixel-by-pixel labeling, providing precise control for detailed annotation tasks.
• Fill: Automatically labels connected regions of pixels with the same category, speeding up the annotation of uniform areas.
• Superpixel: Divides the image into clusters of pixels (superpixels) based on similarity, enabling efficient labeling of larger regions while preserving image structure.

Raster Mask

The output of this type of annotation takes the form of a greyscale PNG mask for each image, where the value of a pixel represents the category to which it has been assigned.

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The location of the raster mask image is listed in the output JSON file under the mask_url key:

Annotation Output

Review the recipe to see the full JSON

...
"layers":{
           "raster_coding":{
              "mask_url":"https://mask-image.png"}
...
}

aws s3api get-object --bucket samahub3 --key "clients/23/external/projects/8616/tasks/6125acb53928ed00e6fc55fa/outputs/Image Raster/d1bdde4b-3d01-47d3-8536-4952eb871969.png" raster-mask.png
                

	The linked Colab notebook contains Python code that shows you how to get your latest deliveries, get temporary AWS credentials, and download the raster masks.