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Transform massive spatial data into manageable information. Use a distributed architecture to analyse and display large volumes of data. Ingest real-time data from sensors, social media feeds and IoT systems. Let the Esri Geospatial Cloud automatically scale your analysis and data storage.

Visualisation


Reveal spatial patterns by generating on-the-fly data representations that animate and auto-aggregate as your real-time data streams. Use time as a variable to visualise massive datasets in three dimensions.

Big-data analytics


Use distributed computing to analyse data that was previously too big or complex. Identify data patterns that were previously hidden in noise. Find clusters of events and hot spots of activity. Use regression tools to find relationships between datasets and predict future events.

Imagery and raster analytics


Make sense of massive imagery collections. Use distributed processing and deep learning for object detection, classification, terrain analysis and change detection on a global scale. Execute advanced, customised raster analysis in a scalable environment.

Real-time analytics


Ingest data from sensors, social media feeds, and IoT systems. Analyse real-time data as it streams and trigger alerts as assets move or change. Use drag and drop tools to configure, design and deploy your real-time analysis workflows.

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Case Study

Chesapeake Conservancy


The Chesapeake Conservancy is creating new higher-resolution data and deeper understanding to revolutionise the management of the Bay environment.

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