It’s no secret that the growth of IoT is driving the data explosion, with the International Data Group predicting that by 2025 there will be more than 41 billion connected IoT devices creating just under 80. zettabyte of data. This unprecedented stream of data, coupled with the development of AI to pull insights out of it, will create a much more informed mobile industry than ever before.
AI is often deployed to automate map creation using location intelligence, giving businesses the opportunity to enhance the insights they gain using true location-aware AI. . This means AI can understand the properties of location information and allow developers to leverage this location insights into their apps and products.
AI transmitting location data is used to generate pattern identifiers and location signatures from the data it collects, which can aid in creating HD maps and realistic simulators to visualize this data. From understanding how consumers move, to knowing where best to drill for oil, these clever visualizations are creating an exponential number of use cases across countless industries.
Value in building reliable AI-based location intelligence services
The AI value chain has changed dramatically over the past few years. We are transitioning from traditional machine learning (ML), where the value is mainly revolving around the solution model architecture and the algorithm, to the stage where the value lies in having a benchmark model working on your data and can grow, compile and scale it.
We are accustomed to implementing standardized AI and ML to collect location information, and now use sensors, bridges or satellites to create standard definition maps, such as people with on their smartphones have become popular. We’re increasingly moving towards high-resolution (HD) maps, built by machines to service machines, making it possible for the machine to deploy this data for specific use cases, such as building algorithmic solutions for autonomous driving. This new approach makes it possible to combine multiple sources to realize features and patterns, handle both static and real-time events to predict behaviors and conditions.
A prime example of this is AI / ML powered map, where the end-to-end process generates a self-healing map based on continuously collecting ‘low-level’ observations and ‘advanced’ synthetic map features, both auto-learning features. They work synchronously to develop and adapt each feature of the map, such as signs, lanes, and sidewalks, and are learned for each geographic area. However, this shortcoming lies in its hardness, as it negates the nuances around data collection, which is why location data platforms are innovating to create AI receivers. know the location.
AI makes possible predictions
Simply put, this is an AI designed to understand the dependencies and properties of the location information it receives and produce more advanced insights. Real-time semantic relationships between physical objects are a key element of this and can be used to construct location charts, which are geospatial-temporal representations of the world. gender.
These representations can be used to make informed predictions through the use of real-time data, such as weather, traffic or sensor data. AI / ML will facilitate understanding of position and movement in context, and this will provide us with new approaches that fundamentally change the way location information is collected. and use.
AI infused with location data can discover new data patterns and create more precise data patterns it collects. This means it can be used to render key features and aggregated with other data in ways not attainable with traditional AI approaches, such as using taste AI. to predict the NO2 concentration correlation using traffic data.
Beneficial for the transport and logistics industry
Data collected through AI can be used to inform and help make decisions on a particular issue, as seen in its use in the transport and logistics industries. The key challenge facing the industry is the need to tackle large-scale optimization. Utilizing large amounts of data in large supply chain networks creates a problem of optimization for data providers due to the large number of suppliers, consumers, and interventions. concerned. While intrusion steps have already been made in this space, there is still room for growth through the use of location-aware AI. This technology is increasingly embedded throughout supply chain networks, increasing the number of interfaces for monitoring and monitoring.
Reinforced learning (RL) has the potential to be a huge leap forward in the transport and logistics industry. This technology enables simulation and sensitivity analysis that can be used to generate predictive models and simulations. RL can help us find new ways to control fleets, allowing for more optimized fleet traffic. An example of its use could be to plan and manage the flow of traffic in major smart cities with the ambition to reduce CO2 emissions, while ensuring safe and efficient mobility. max. Insights learned from these models enable data-driven and outcome-driven decision-making for an efficient distributed network.
Significant margins for growth
The potential of location-aware AI is substantial, and no one can claim to have used its full capabilities in logistics and transport spaces, cars or smart cities. The greatest advances in AI / ML may not occur in closed laboratories, but only through a set of robust open innovation and open ecosystems.
Collaboration between location platforms and academic or governmental institutions can play a key role in this, as it can leverage AI in an effort to drive multiple use cases for one ‘. Smarter planet ‘. The navigation platform can deploy smart city initiatives related to public safety or provide key insights to companies in the automotive and mobility sectors. These insights could be considered a new currency in the mobile space and used to make important strides towards building real-time AI-based location intelligence services. and reliable. In return, these services will enhance products on cloud, edge, and device deployments for many years to come.