Here’s an interesting look on the use of AI and machine learning in the geospatial world.¬† Given the huge datasets found in remote sensing, it’s not surprising to see that field leading the way in cutting edge data analytics.

From a geospatial perspective, machine learning has long been in wide use. Remote sensing datasets have always been large, so the large data processing power of Machine Learning has been a natural fit. For example, processing satellite images using K Means or ISODATA clustering algorithms was one of the first uses of remote sensing software.

Ricky Brundritt, PM in the Azure Maps team, walks Olivier through data driven styling with Azure Maps. Data driven styling allows you to dynamically style layers at render time on the GPU using properties on your data. This provides huge performance benefits and allows large datasets to be rendered on the map. Data driven style expressions can greatly reduce the amount of code you would normally need to write and define this type of business logic using if-statements and monitoring map events.

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In this video, learn about the heat map and image layer visualizations in side of Azure Maps. Heat maps are used to represent the density of data using a range of colors. They are often used to show the data “hot spots” on a map and are great to help understand data. The heat map layer also supports weighted data points to help bring the most relevant information to the surface.

The image layer allows you to overlay georeferenced images on top of the map so that they move and scale as you pan and zoom the map. This is great for building floor plans, overlaying old maps, or imagery from a drone.

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