A Cloud Lighting Audit Service Built on Scalable AI Algorithms
Through Phillips Interact City running a smart audit has become even easier as Signify sales personnel can use AI photo detection for a virtual audit instead of a time and labor intensive on-site one.
Integrations
– Geographic Information Systems (GIS)
Inadequate Information for Infrastructure Decisions
Through AI based image detection, determining the type of light points available becomes very simple. This in turn makes it easier to take infrastructural decisions on the same. Through image detection the Signify personnel will get to know the pole material, height, and count along with the fixture technology and style – all without moving from their desk!
Easier Classification of Light Points Type Via Image
Through AI-powered image detection, Phillips Interact City can classify the light point types on the basis of the street-level images of those positions. Thus, it is easier to know which type of light points are present in a particular part of the city without having to manually check the same.
Automatic Generation of Light Map Views for
Smarter Working!
Smarter Working!
With the automatic generation of light map views, Signify personnel can check lighting requirements against standards and utilize the audit data that is augmented through Google Street View. This will give a virtual view of the road size/width, number of lanes, height of the poles, the lighting type, etc.
Poor Lighting Assessments in Urban Areas
It is difficult in an urban setting to know whether a given location has sufficient lighting without physical investigation of the same e.g., open parking lots. However, due to the recognition and reporting features, the Signify personnel can generate reports of a specific location including the number of active light points. This improves the accountability of existing light points and optimizes their use all through AI and automatic light map generation.
AI Powered Lighting Solutions Through Phillips
Interact City
Interact City
When it comes to urban infrastructure management, proper lighting is integral for safety and optimal functioning of city facilities. Understanding this, Phillips had Sunflower Lab assist in the creation of an algorithm-based AI image detector that can even report lighting levels viz. On/Off/Bright/Dim.
AI Enabled Virtual Audits with CNN for an Automated Approach
Virtual audits eliminate the need for onsite investigation which is time consuming and cumbersome. Through visual search (CNN) the template can be automatically chosen based on the details the AI is able to present. Thus, through an automated approach the entire city’s infrastructure can be managed using less time and resources.
Limited Visibility for City Infrastructure Management
Through Phillips Interact City Smart Audit, it is possible to keep an eye on your city from behind your desk. By simply providing the zip code Google Street view will return a map that will display the type of light points along with the technology that is being used.
Scalable Virtual Audits Through AI – Know Your City without Leaving Your Desk!
AI empowered and scalable, Phillips Interact City uses Google Street View in the assimilation of multiple images of the light point from various perspectives. This builds a 3D model that can be matched by machine learning. Thus, the Signify personnel can interact via the application & get a virtual take on the city’s lighting infrastructure.
Mapping Light Points and Auditing Them was Never This Easy
Through the innovative use of Google Street View and energy data along with public GIS data, it is possible to replace traditional audits with virtual ones. Furthermore, through mapping one only needs to look up the zip code in order to access the required data.
How Interact works
Hardware + software + services = light and insight
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Speed & Accuracy Through AI-based Photo Recognition
Instead of the need to go onsite to recognize the type of lighting asset, ole or its condition, Phillips Interact City Smart Audit provides a smarter solution. With the help of AI-based photo recognition it is now possible to know the lighting types along with its condition and whether it needs repair or replacement.
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Merging Machine Learning and Statistical Profiles Through Smart Audit
AI has proved itself to be a quick learner, hence, the machine learning curve is an integral part of the mapping and audit process. Incorporating Google Maps for the auto location mapping of the asset in the database, it has become possible to identify the type and health of a light point without physical inspection using other data too. By the creation of statistical profiles, it is easier to create estimates and scale up for large cities.
