Map creation now comes with Machine Learning
HERE Technologies’ HERE Workspace is expanding to give enterprises more ways to integrate spatial intelligence into their business operations, supply chains and fleets.
Launched two years ago as a platform tool for building and scaling customized maps, services and experiences, HERE Workspace is offering new and improved capabilities, including a low-code environment for developing spatial intelligence and a new intuitive and predictable value-based pricing model. HERE is also pleased to announce that HERE Workspace now integrates seamlessly with Amazon SageMaker, enabling users to leverage familiar value-added Machine Learning tools to enhance their spatial intelligence development.
“We believe that every smart enterprise will want its own private map, leveraging its own spatial data at scale,” says Giovanni Lanfranchi, Chief Product & Technology Officer at HERE Technologies. “Building on our progress of the last years, we’re expanding the possibilities of HERE Workspace by connecting it to Amazon SageMaker, an end-to-end machine learning solution, to deliver even greater value for customers.”
Low-code, high-grade spatial intelligence with Machine Learning
As businesses generate large volumes of raw spatial or map data, HERE Workspace helps them convert it into standardized map content that can be connected to the HERE map. Dedicated low-code templates and a drag-and-drop workflow editor enable customers to automate the ingestion of data as well as conflate, validate and publish map content for their own private use – taking full advantage of HERE’s advanced mapmaking capabilities and powerful location services and SDKs. The result: navigable, actionable maps which serve as a basis on which to create private and customized experiences.
For example, by mapping their private facilities, businesses with logistics operations can enable more intelligent routing and accurate estimated time of arrival (ETA) predictions. If a journey begins inside a ferry terminal and ends inside a factory, ETA calculations should represent the true expected travel time, not only the portion of the operation completed on public roads. HERE Workspace also supports location contextualization of a business’s data to uncover new insights – such as through observing fleet speed versus road speed limits to assess risk. Alternatively, a business might layer its own private places and points of interest on the HERE map and activate a private search and routing algorithm that accounts for these new locations.
Multiple organizations are already leveraging private spatial intelligence to support their unique business goals, logic and use cases. For example:
- A leader in online commerce runs its own private routing layer on the HERE map to ensure its trucks only drive certain roads in and out of its logistic hubs.
- A global automaker is creating a private map layer of automated driving zones that tell its vehicles and drivers where exactly Level 2 and Level 3 automated driving is permitted.
- Rural firefighters are mapping trails and tracks, including across private land, to support rapid access to remote areas to tackle wildfires.
- A leading ride hailing company uses private custom locations to guide drivers to the best pick-up and drop-off places.
HERE Workspace now connects with Amazon SageMaker
Demonstrating its extensibility into other capabilities and solutions, HERE Workspace now connects with Amazon SageMaker’s machine learning tools to enable businesses to harness the best of both HERE and Amazon SageMaker. By integrating SageMaker into HERE Workspace, customers can now train models based on business activity, deploy to SageMaker and then seamlessly use models in HERE Workspace to extend their impact to map or services customization.
At AWS re:Invent 2022, HERE will be joining AWS on stage to demonstrate how we have integrated Amazon SageMaker and to illustrate how Amazon SageMaker can aid predictions of how long traffic jams will last. As part of the demonstration, HERE will highlight the use of Shadow Deployment, a newly launched Amazon SageMaker feature for evaluating machine learning model performance without impacting production traffic or workload.
“By integrating Amazon SageMaker into HERE Workspace, our customers can now leverage the breadth and depth of Amazon SageMaker’s machine learning services from within the HERE Workspace environment,” said Ankur Mehrota, Director, Amazon SageMaker at AWS. “Customers can augment their spatial intelligence efforts by using their data in HERE to build, train and deploy machine learning models in Amazon SageMaker through a direct integration with SageMaker Studio. We’re excited to help our joint customers accelerate innovation and time-to-value by bringing our capabilities together.”
New value-based pricing model
HERE Workspace also introduces an intuitive new business model which aligns more closely with the value businesses extract from HERE Workspace. Our new business model is based on subscriptions and makes expected costs a lot easier to predict thus providing a more user-friendly experience.