DATA is available on the Trimble Learn platform: https://learn.trimble.com/learn/publi...

Image Object Level (IOL) hierarchies is a data structure that incorporates image analysis results, which have been extracted from a scene.

An image object is a group of pixels in a map. Each object represents a definite space within a scene and objects can provide information about this space. The first image objects are typically produced by an initial segmentation.

Within a project you can create multiple Image Object Levels, creating an Image Object Level hierarchy.

Every image object is networked in a manner that each image object knows its context – who its neighbors are, which levels and objects (superobjects) are above it and which are below it (subobjects). No image object may have more than one superobject, but it can have multiple subobjects.

We will use this concept for extracting impervious surfaces on a sublevel and use this information to classify image objects on a superlevel representing parcels.

_____________Video Content________________

00:00​ Introduction & Theory
03:30​ Exercise Overview
06:31 Exercise Handson
10:30 RS Create SuperLevel "Parcels"
18:00 RS Create SubLevel "Analysis"
20:55 RS Classify Impervious surface on SubLevel
30:47 RS Classify SuperObjects based on SubObject statistics
38:02 RS Export Results
47:19 HOMEWORK ;)


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