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PlaneInsight is a Port of Seattle computer-vision pilot for the air-cargo area at Seattle-Tacoma International Airport (SEA). It uses existing security-camera video and data-center resources to identify aircraft and selected ground equipment. The Port’s goals included improving efficiency, reducing delays and strengthening accountability with cargo carriers; available reporting describes those as aims, not as quantified results.
What PlaneInsight detects
PlaneInsight analyzes images from the airport’s existing security cameras. The reported system can identify the type, location and approximate outline of aircraft and selected ground equipment, including ladders, ground power units and belt loaders. At a gate, it can determine whether an aircraft is docked, describe nearby objects and read visible text such as an airline name.
The project’s core model was described as a convolutional neural network using transfer learning. It is an applied computer-vision tool for observing cargo-area activity—not a general-purpose cargo-management platform.
How the Port built and deployed the pilot
Port CIO Matt Breed and senior systems architect Skip Tavakkolian began exploring machine learning and computer vision in 2016. Tavakkolian and Chris Evans, a general foreman for aviation and electrical systems, developed a proof of concept in 2017. A Port innovation-pitch process helped secure business sponsorship, and the Port deployed the PlaneInsight pilot in 2019. A CIO interview published March 10, 2020, reported that it had been operating since deployment.
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Rather than install a new, dedicated camera network for the pilot, the team drew on existing infrastructure. Tavakkolian described using security-camera video streams and the Port’s data center to collect snapshots, assemble training datasets, train neural networks and run image inference—the analysis of images.
Why training data and staff expertise mattered
Computer-vision models need examples of the objects they are meant to recognize. The 2020 account says there were no standard training datasets for the aircraft and ground equipment in PlaneInsight’s scope. Workers therefore annotated tens of thousands of images, labeling objects, drawing bounding boxes and, in some cases, tracing object shapes with polygons.
To help with that work, the Port started a high-school summer machine-learning internship. Tavakkolian said interns created nearly half of the dataset; that figure is his estimate, not an independently audited breakdown.
The project also required staff to learn machine-learning and computer-vision concepts, frameworks such as TensorFlow, and ways to explain the technology to colleagues. Tavakkolian identified learning the concepts and tools—and educating others—as major challenges. The example suggests that reusing cameras and computing infrastructure can avoid some new hardware needs, but it does not remove the work of building domain-specific data or developing staff skills.
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What the Port hoped to improve—and what is documented
The air-cargo team expected PlaneInsight to help increase efficiency, reduce delays and improve accountability with cargo carriers. Tavakkolian described the pilot as helping the team improve efficiency, but the March 2020 account reports no baseline, measurement period, percentage improvement or causal evaluation. There is no published quantitative performance result in the cited accounts, so a specific delay reduction or productivity gain cannot be claimed.
The project’s documented value also included organizational learning: greater awareness of machine learning at the Port, experience applying computer vision and identification of possible follow-on applications. At the time of the 2020 report, the cargo team wanted to explore automatically inventorying equipment, checking actual activity against scheduled events and measuring schedule variance. These were prospective uses, not confirmed completed features.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How PlaneInsight differs from other SEA technology projects
Other Port and university accounts describe related airport technology, but they should not be treated as PlaneInsight capabilities:
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- Surface-area management: A separate computer-vision system monitors ground handling and servicing around aircraft parked at gates.
- APU monitoring: A distinct research effort used acoustic sensors to detect aircraft auxiliary power unit (APU) use. The University of Washington’s Corgo project page says a student team worked with SeaTac Airport, installed three sound-gathering devices, analyzed APU sounds with a trained machine-learning model and displayed results on a custom dashboard. The page reports the project was completed December 15, 2021. This audio-sensing project is not PlaneInsight’s cargo-area camera system.
The approaches answer different questions: cameras can identify visible aircraft and equipment, while sound sensors can monitor an acoustic signal such as APU operation. The accounts do not provide a head-to-head performance comparison.
Quick Recap
Sources
- CIO, “Port of Seattle uses AI to optimize air cargo operations” (March 10, 2020): PlaneInsight’s design, timeline, staffing challenges and reported aims.
- Port of Seattle, “Airport technology” (2022): context on hardstand monitoring, surface-area management and the separate APU research effort.
- University of Washington Global Innovation Exchange, Corgo project: details of the separate acoustic APU-monitoring project and its completion date.
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