1. Visualisation of the (in)accuracy
Collection
Curation
Cookbook to do the survey (quality, completeness, accuracy)
As-Built required deliverable - ASCE white paper.
Feature naming & structure (Network, Location, time, Semantic)
Integration of unstructured data
Multiple acquisition methods (radar, lidar, RFID, Survey, …)
Innacuracy or lack of data
A lot of research to refresh data and collect data
Convertion from paper map to digital data
Take into account movement of soil ("known")
Continuous acquisition
Link to common data model
Framework/ Fundamental
Common Platform, SDI, Centralized Access portal (Discovery, Use,
Use Cases
Risk Analysis (Insurance Use case)
Linked to the use case
Linked to uncertainty of data
Deep learning for NYC risks. IEEE paper.
Fit for purpose (2D, 3D, 4D)
Visualisation of the (in)accuracy
Visualisation of terrain as important as network
Analysis
Viz.