Create an ID segmented images
From what I understand, your segmented images come with 3 channels, where the color of each pixel corresponds to its GT label.
When you train your image segmentation model, there is no need for an output of 3 channels (its redundant), so they recommend that you create a new annotated image, where you replace each color with the provided i.d.
This recommendation is simply there to make the job of the model a little bit easier.
I have a dataset containing both RGB and Segmented images as ground truth , the readme.txt included in the annotated dataset stated this :
GT_color :folder containing the groundtruth masks for semantic segmentation 
Annotations are given using a color representation, where each corresponds to a specific class. This is primairly provided for visualization. For training, create a corresponding ID image by assigning the colors to a specific class ID as given below.

Class       R   G   B   ID

Void            -   -   -   0

Road            170     170     170 1

Grass           0   255     0   2

Vegetation      102     102     51  3

Tree            0   60  0   3

Sky             0   120     255 4

Obstacle        0   0   0   5

I don't understand what's meant by "creating corresponding ID image" , aren't the segmented images labelled already by each area's color ? meaning that the rgb are the labels ?