Image Management and Analytics integrated in geospatial workflows

Traditional imageery workflows in many cases still follow a Datset --> process --> result Dataset scheme.
In todays world of high spatial and temporal imagery resolution and analytical cadence, this turns out to
be increasingly inpractical. This is backed by todays cloud based mass data storage and constant change detection requirements.
So accessing Imagery as dynamic compilations and analyzing stacks, time-series, blocks upon demand, largely without producing
permanent internmeidate data has become a paradigm. Added to it is the demand to analyze the Imagery sources together with in-situ
sources, sensors, base geometries and other georelated data sources.

This presentations will outline the capabilities and strategies of the ArcGIS Platform to fulfill these demands and reference
cloud based data and processing as well as dynamic server analytics and traditional Desktop approaches - UI and Python/Notebook driven.
Time-series analysis, DeepLearning/AI and combined Raster/Vector analysis will be used as examples. 

Target audience is anyone interested in geospatial analysis combining Imagery and other geospatial data sources.