3D Mapping Solutions for Data Visualization

Business challenge

​Our client is a key global player in 3D mapping solutions, empowering OEMs, enterprises, smart cities, and transportation systems worldwide with innovative cloud-based 3D mapping services. For the company, putting their location data to work was a tough challenge. They realized that raw data generated by sensors could become more valuable when turned into visuals that are easier for people to grasp.

Our client has taken on the mission of creating a full data ecosystem for real-time location data analytics applying 3D mapping solutions. Their ambition for the location management platform has been to provide:​

• ​A comprehensive library of highly accurate, on-time, and reliable location-related data
• A strong technology foundation for companies and developers to build location-enriched, data-driven services and solutions
• Accelerated development and time to market of groundbreaking location-centric products
• A hub for global location data exchange, interaction, and enrichment
• A powerful framework for geodata processing and sophisticated analytics​

Through years of working with routing, mileage, mapping, and navigation solutions, Intellias has accumulated extensive expertise in mapping solutions. It was a natural choice for our client to select us as their technology partner to tackle these challenges of rendering maps for visualization.

Powered by location intelligence, our client’s solution is now an open B2B cloud-based big data analytics platform that has become the go-to tool for major location players and the powerhouse behind hundreds of intelligent 3D mapping solutions. Our client continues to expand their platform’s capabilities. The next step is to implement and finalize efficient data visualization.

3D map rendering solution delivered

​For over two years, Intellias has been contributing to the development of transportation solutions and data visualization services for our client’s location platform for rendering maps. We started out with a team of four, and as the project gained momentum, we grew to a ten-strong team. The concept debuted as a pilot project to build 3D maps with the help of vector graphics. Initiated by our client’s Chief Technical Officer, the project was later handed over to Intellias engineers to build on the existing 3D map rendering engine library.

Our primary goal was to create a proof of concept (PoC) to demonstrate how 3D mapping services can add value by visualizing location-centric data in a vector format. We spent the first four months developing this use case and came up with a demo that won high acclaim during internal presentations held by all departments of our client’s organization. Our next important milestone was to implement the product-level solution. ​

The platform provides several key data visualization components:

• Catalogs and layers – All geospatial data is organized into catalogs containing layers. These layers represent different types of data and can be overlaid to construct a complete digital map. Layers are also used to segment data based on semantics. For example, a catalog can contain a map that includes layers for road attributes, topology, and signs.
• Notebooks – Data analysts can do ad hoc development and data analysis using a notebook and a web-based environment to transform and analyze data. Notebooks help data scientists build machine learning algorithms to make customized routing recommendations based on current conditions.
• Visualization library – This is a TypeScript library for rendering 2D and 3D data on a map to make sense of large sets of geospatial data and identify gaps in data coverage.

To equip developers and simplify their product implementation, the platform provides SDK tools to write the production pipeline, upload it to the platform’s cloud for testing, and seamlessly integrate a new 3d mapping services into a custom app. Users can create their own JavaScript plugins to convert large binary data streams into a suitable format for observation, validation, and processing.

The next stage was to create a data processing pipeline and integrate it into our client’s mapping data platform. The new pipeline uses map data as its source and converts it into vector tiles for a convenient usage. We migrated data pipeline from the legacy data source to the new one supplied by our client’s platform data visualization services. The new data source combines multiple data layers, including linear and polygonal features as well as road, lane, navigation, address, and other attributes.

Next, we configured the data pipeline to run incremental map updates and compilation. This has accelerated the compilation process by recompiling only those individual parts of the map which are relevant for the update. Our compilation team also created a data validation library for detection of errors in the compiled vector tile data before use site visualization.

To take the full advantage of open web maps as an output and client-side rendering performance, our team is preparing simplified geometry data, covering lines and polygons representing administrative borders, water bodies, roads, and more. As a result, Intellias engineers have come up with an optimized data specification that includes rendering-friendly cartographic attributes.

Business outcome

Last year, our client showcased their capabilities in 3D map rendering  at the world’s largest technology show, CES 2018. The open location platform received positive feedback from industry insiders and influencers as a compelling product not only for automakers but also for organizations from any industry that relies on geolocation data and services, including insurance, logistics, IoT, machine learning, retail, and urban planning.

The solution we’re developing brings these advantages to our client:

• ​Provides access to highly accurate, rich, and up-to-date location-related content and services, including built-in map matching, geocoding, routing, and weather and traffic data
• Supports the development of world-class, location-centric applications
• Establishes a secure and scalable marketplace that simplifies data exchange within the ecosystem and speeds up time to market
• Allows users to manage their data visibility and control privacy and licensing
• Provides critical information that autonomous vehicles need to maintain road safety
• Offers an environment to perform interactive data analysis and develop machine learning models

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