The Superior Technology for Airside Environments
Unlock the full potential of your airside operations with Assaia's ApronAI Suite powered by Computer Vision. Our cutting-edge technology detects events, key milestones, and people in real-time, surpassing the capabilities of IoT sensors.
In this article, we outline the technical and operational benefits of Computer Vision, specifically using cameras as primary sensors. While we firmly believe in the cost-effectiveness and feasibility of our approach, we acknowledge that there is no one-size-fits-all solution. The choice of technology depends on your unique needs, and sometimes a combination of different technologies is the most suitable solution. This document aims to guide you in selecting the right technology for your specific case.
Technical Comparative Analysis
IoT sensors require constant power, either from exchangeable or rechargeable batteries, or by connecting to the Ground Service Equipment's electric network. While the latter is optimal, it involves significant upfront work. Battery-driven options increase operating costs and compromise data quality due to lower power consumption.
In comparison, cameras require a one-time installation and can be used for years without maintenance. No energy source issues or additional costs to worry about.
Sensors only collect data for the units on which they are installed. While it's possible to install sensors on every piece of equipment, certain objects like luggage, small maintenance materials, people, and liquids cannot be equipped with sensors.
In cases where a large geographical area needs to be covered, sensors are preferred. However, for limited areas like airports, cameras can be equally effective. Cameras also offer the advantage of providing a full understanding of a 3D object's position and its surroundings, such as identifying people walking under aircraft wings.
Data Points/Sensor Ratio
Sensors often capture only a single property of equipment, while cameras can capture infinite amounts of information with advanced video processing algorithms. However, cameras have limitations in capturing data points outside their line of sight. Additionally, certain variables like water pressure and fuel levels can only be measured by sensors.
Cameras excel in capturing variables like indoor tracking, human postures, and object interactions with higher reliability.
Business / Operational Comparative Analysis
When it comes to evaluating performance using data collected on Ground Support Equipment (GSE), there is often resistance from workforce employees to being tracked. In some cases, employees have intentionally damaged sensors to avoid being monitored or because they disagree with changes in the process. This behavior not only reduces data coverage if sensors are broken, but also adds to the operational cost of the system due to the need for frequent repairs. We have even witnessed cases where only a small fraction of GSE had functioning sensors, rendering the system useless and resulting in manual process management.
Camera-based computer vision systems are not susceptible to this kind of user behavior. They are out of reach of staff and not exposed to any potential damage, which increases uptime and reduces operational costs. Additionally, camera-based systems offer higher system availability over the long-term, leading to a stronger return-on-investment for the operator.
When it comes to tracking objects in airport apron areas using sensors, two main installation costs need to be considered.
Number of Sensors
Equipping hundreds or thousands of pieces of GSE with IoT sensors can be costly, as each unit requires its own sensor. In contrast, camera-based computer vision solutions can utilize existing camera infrastructure at airports. Cameras have a one-to-many relationship with the objects they track, significantly reducing the number of cameras needed compared to IoT sensors. Furthermore, the price of cameras has significantly decreased in recent years, making them a more economically advantageous choice over IoT sensors.
To function properly, IoT sensors require a communication network. Powered GSE can use existing networks such as cellular networks, although these consume a lot of energy. However, non-motorized GSE often require sensors that communicate over low-power wide area networks (LPWAN), which are usually not available at airports and would need to be established.
For cameras, both existing ones and new/additional units, can be integrated into any existing camera network at typical installation locations. Data networks and power are readily available, eliminating the need for additional civil works and reducing network installation costs.
Maintenance and Operational Costs
IoT sensors are essential for tracking and monitoring Ground Support Equipment (GSE). However, they are prone to damage during operations, requiring repairs or replacements. This not only necessitates taking the GSE out of service but also requires a dedicated asset management team to ensure sensor availability and address malfunctions. Additionally, battery-powered sensors need periodic replacement, leading to intermittent operations.
Camera systems connected to cables require minimal maintenance and can operate continuously for years. Any necessary maintenance can be remotely done without interrupting the service.
Practical and Political Considerations
Equipping each piece of GSE with an IoT sensor can be challenging, both practically and politically. External parties, such as ground handlers, may be resistant to being tracked, particularly if the data is used to evaluate their performance. This has hindered the widespread adoption of IoT sensor solutions in the industry, especially when different parties are involved. In contrast, camera and video analytics solutions require less cooperation from the objects being tracked, making them more feasible for airports. These solutions can be implemented unilaterally or through collaborative efforts with airlines and ground handlers.
A camera based solution has the ability to capture a lot of information about everything that is taking place in the field of view of the camera. As an example, a GPS tracker can tell you that a belt loader has arrived at a specific stand. However, the GPS tracker can not tell you if the belt loader is connected to the aircraft. And whether it is actually operational. And whether it is loading or unloading bags. Additional sensors would be required to collect all of this information. With a single camera however, all this information (and more) can be gathered.
On the other hand the camera does have one important limitation when it comes to asset tracking and that is its dependence on its field of view. Even though it is possible to identify unique objects with the camera and to track movements from location to location, a detailed tracking including complete path/routing is better done with a GEO location tracker.
There is no one-size-fits-all technology for every problem. The choice between IoT sensors and camera-based systems depends on the specific needs. At Assaia, we prioritize understanding our customers' problems and offer the most suitable technology solution. We collaborate with various IoT sensor partners to ensure that we provide the best solution to our customers.
We are pleased to partner with Assaia to implement the ApronAI Turnaround Control solution at T4. This new solution will not only optimize operations and our work with our business partners, but will also help us to ensure a first-class customer experience at T4.
For most airports, the apron is a a black box. Assaia finally gives our ground staff full insight into every turnaround. This allows them to focus on what really matters, while simultaneously making the work environment safer.
The real-time and historical insights that can inform both airport and airline operations make this solution a clear winner for everyone.
Assaia's product allows airports and airlines to collect, track, and analyze data in real time; this innovation removes inefficiencies and optimizes performance.
We’re creating the airport of the future, and innovation in apron operations will directly improve the passenger experience. We are laser focused on innovations that will make Pearson and its whole apron ecosystem more efficient while reducing our carbon footprint.
This data provides the single source of truth covering all turnaround operations. It is, therefore, an integral part of our Airport Collaborative Decision-Making initiative.
SEA needed an innovative solution to our capacity problem and have worked with Assaia to optimize the turnaround process resulting in reduced taxi times and increased passenger satisfaction. Assaia has exceeded our expectations, consistently delivering on-time & on-budget.
With the help of Predicted Off Block Time from Assaia. JFKIAT Operations can be proactive to reduce or eliminate any delays and gate holds
We are proud to be partnering with the Assaia team in our mission to use technology to improve the efficiency and safety of the airport environment.
I had mentioned before, great innovation on your part. With these types of improvements, T4 is always leading at JFK. Thank you
We’re working hard on becoming an airport of the future, and this involves rethinking every part of our ground operations. Assaia’s ApronAI is an integral component of our vision for the ramp of the future.
Assaia’s technology adds critical data points to CVG’s early-stage neural network for operational advancements. Structured data generated by artificial intelligence will provide information to make decisions, optimize airside processes, and improve efficiency and safety.