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Microsoft and Esri launch Geospatial AI on Azure

Microsoft and Esri have come together to offer the GeoAI Data Science Virtual Machine (DSVM) as part of Microsoft’s Data Science Virtual Machine/Deep Learning Virtual Machine family of products on Azure. This collaboration will bring AI, cloud technology and infrastructure, geospatial analytics and visualization together to help create more powerful and intelligent applications.

At the heart of the GeoAI Virtual Machine is ArcGIS Pro, Esri’s next-gen 64-bit desktop geographic information system (GIS) that provides professional 2D and 3D mapping in an intuitive user interface. ArcGIS Pro is a big step forward in advancing visualization, analytics, image processing, data management and integration.

ArcGIS Pro is installed in a Data Science Virtual Machine (DSVM) image from Microsoft. The DSVM is a popular experimentation and modeling environment on Azure that provides a host of AI, machine learning and data science tools. These are all conveniently pre-configured for immediate productivity. The DSVM can run either on CPU-only VM instances on Azure or leverage GPU based VM instances which are particularly useful when training large-scale deep learning models.

The Geo AI Data Science VM extends the AI and data science toolkits in the Windows Server 2016 edition of the Data Science VM by adding ESRI’s ArcGIS Pro and interfaces in both Python and R to help data scientists leverage the spatial data, rich GIS processing, visualization and analytics in ArcGIS Pro to create better AI applications. For the geospatial analytics professionals, this product now brings in powerful new AI and predictive analytics capabilities including deep learning and machine learning algorithms. Deep learning algorithms are very effective in understanding image/raster data, time-series, and unstructured textual data. The GeoAI Data Science VM also makes it easy to develop for Azure and use big data services like Apache Spark within the VM for analytics. All the tools are pre-installed and pre-configured so that data scientists and geospatial analysts have a ready-to-use environment.