Computer Vision in Infrastructure

From infrastructure monitoring, traffic planning, and waste management to security, AI-powered computer vision from LandingLens has the potential to make a promisingly safe environment.

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Applications for Machine Learning in Infrastructure Management

LandingLens makes it easy to build and scale computer vision systems to transform visual data from cameras and other IoT devices into usable, actionable data. Computer vision in infrastructure management helps urban planners, construction firms, and government agencies to understand and extract key information from huge volumes of images, to help provide the data to empower improved decisions.

Railroad Inspection

Railway systems require constant attention and maintenance to ensure passenger safety. With LandingLens, regular inspections (e.g., switch point welding, rail lubrication, and switch cleaning) and emergency repairs can be identified early, preventing potential hazards. By using machine learning in infrastructure monitoring, inspectors can keep railway systems in top condition for years to come!

Facilities and Infrastructure

For property developers and infrastructure managers with thousands of facilities in their portfolios, LandingLens offers an efficient way to have eyes on properties at all times. Powered by AI, the LandingLens platform can monitor multiple facilities around the clock. Image recognition can be trained to recognize footfall, occupancy, damage, intrusion, and identify almost any non-normal pattern. With the LandingLens platform, infrastructure such as power substations, pipework, cables and more can be monitored, secured, and protected rapidly and easily.

Public Safety

Public agencies can make use of real-time object and anomaly detection by training the LandingLens platform to detect specific items and behaviors. For example, LandingLens can be trained to identify anomalies that can cause safety issues. With AI-powered deep learning recognition, LandingLens can highlight safety issues in infrastructure to enable appropriate actions for intervention before situations escalate.

Construction Site Safety

Construction companies can use AI-powered computer vision to automate safety monitoring and reduce the risk of injuries on work sites. For example, LandingLens can recognize when workers are not wearing the correct personal protective equipment, such as an absent helmet, and send supervisor alerts. Not only will improved monitoring reduce the number of unsafe situations, automated safety monitoring enabled by LandingLens also helps reduce the risk of non-compliance with relevant regulatory standards, and lessens the risk of injury

Traffic Monitoring

With LandingLens, image feeds from traffic surveillance cameras can be transformed into valuable data-driven insights into vehicle flow and driver behavior. The LandingLens AI-powered computer vision can capture real-time car count, flow volumes, and more. Similarly, LandingLens can be trained to recognize vehicle types or specific features to enable comprehensive traffic monitoring solutions.

LandingLens Benefits for Computer
Vision and AI in Infrastructure

Achieve New Levels of Productivity

Using computer vision and machine learning in infrastructure monitoring, businesses and government agencies can better leverage their investment in visual data while streamlining their own processes and freeing human talent for higher-level work.

LandingLens removes the limitations of conventional monitoring systems – picking up subtle cues that humans might otherwise miss, remaining fully focused and on task for as long as the job requires, and seamlessly keeping pace with rising data volumes.

Enable Smart Cities and Infrastructure

AI-powered computer vision can become the eyes of the smart city, streamlining the processing and analysis of large amounts of complex visual information. The LandingLens AI-powered platform can enable distributed sensing systems that deliver actionable data from a rapidly increasing torrent of input streams.

With LandingLens’ AI solutions for infrastructure monitoring, it is possible to monitor rooftops, cable ducts, rail lines, roadways, buildings on a continuous basis, delivering the performance, efficiency, and scalability required to manage the infrastructure and resources of a smart city.

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