Identifying Infrastructure Change in the 4th And 5th Dimensions

As humans we see the world in one-dimension (a 1D point), two-dimension (a 2D line), and three-dimension (a 3D object). Many will recognise the fourth dimension (4D) as time, which - while not measured in terms of Euclidean space - can be represented by the detectable change in state of a given object between two points in time.

26 Jun 2017
Matthew Coleman, Patrick Carberry, Robert Hoddenbach

Abstract

As humans we see the world in one-dimension (a 1D point), two-dimension (a 2D line), and three-dimension (a 3D object). Many will recognise the fourth dimension (4D) as time, which - while not measured in terms of Euclidean space - can be represented by the detectable change in state of a given object between two points in time. In that sense we cannot see in 4D but as asset managers and operators we experience asset performance and risk implications from it, and in a geospatial context it is more commonly understood through the application of change detection. While 3D addresses the questions ‘what is the object and where is it?’, 4D asks ‘How did it change?’.

Identifying change detection is not new, however the ability to identify change at affordable scale – with detailed resolution and high accuracy across the entirety of a utility’s network - has been made possible by the advent and adoption of cloud computing. Cloud processing data has enabled the use of increasingly sophisticated deep learning computer algorithms which can automatically identify and quantify change in timeframes that are a fraction of those achieved through traditional human-based methods and does not include the inevitability of human error or bias.

Given the limitless potential of analysing big data in the cloud, we are now able to move modelling into the fifth dimension (5D), which postulates all possible scenarios of change between two objects or locations. Now we are asking ‘How could it change?’. This is extremely valuable because it allows us to quickly run complex simulations on extremely large datasets. For example, a utility operator can optimise their vegetation management strategy to meet certain cost or risk targets by virtually testing every possible combination of clearance standards relative to certain types of mapped objects for the entirety of their network.

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