Transform media: Tile

Understanding the "tile" media transformation tool

Paige Kassalen avatar
Written by Paige Kassalen
Updated over a week ago

In short: the tile media transformation tool lets you chop up an image into smaller-sized images. It's sort of like using a cookie cutter to cut the larger image into more manageable ones that are all of the same size. (Note: tiling doesn't apply to videos—see chunk video for that.)

How does the tile tool work?

When you tile an image, you will specify a width (in pixels) that you want the smaller images to be. For instance, if you're tiling an image that's 5000 x 5000 pixels, and you select the new width as 1000 pixels, you'll get 25 tiled images of 1000 x 1000 pixels each. Here's an illustration that helps visualize this.

An example of how a larger image is tiled into many smaller images.

The important thing to note is that the tile tool makes sure that 100% of your original image is included, so nothing falls through gaps. If you have an original image with an odd shape, the tile tool will attempt to make as many "regular" tiled images as it can, and it will use black pixels to fill in around the edges as necessary. Here's another example to help illustrate this.

An example of how empty spaces on the edges of an image are filled in with black pixels when tiled.

Why and when should I tile an image?

Technically, you can tile any image you have in a Dataset, but that doesn't mean that you'll want to.

Like all of our media transformation tools, tiling is generally meant to help make your images easier to annotate. Since you or other users in your account will need to review the image and label the feature(s) you're looking for, you'll want to format that image to make those features as easy to find as possible. Cutting up a really large image into smaller, more manageable pieces will greatly reduce the amount of time you spend looking at any single image when annotating. This is because you won't have to pan and zoom over and over again to make sure you've inspected the entire large image. Believe me, this really does make a big difference!

Therefore, the tile tool is only really useful when you have a really big image and what you're looking for is comparatively very small. For instance, if you're trying to find sedans in a satellite image that covers half of Houston, it would take you forever to pan and scroll and zoom through that entire image! For this reason, we've noticed the tile tool is most often used for geospatial imagery (e.g. satellite, aerial), but it isn't limited to that domain.

Did this answer your question?