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Automating Aviation Chart Creation: A Case Study
Technology Category
- Analytics & Modeling - Machine Learning
- Infrastructure as a Service (IaaS) - Cloud Databases
Applicable Industries
- Aerospace
- Equipment & Machinery
Applicable Functions
- Maintenance
- Product Research & Development
Use Cases
- Leasing Finance Automation
- Time Sensitive Networking
Services
- System Integration
- Testing & Certification
The Challenge
TerraVeta, a Geospatial information firm, was faced with the challenge of manually creating aviation charts. These charts, which provide essential information to pilots such as safety warnings, terrain contours, communication frequencies, geospatial data, and other instructions, had to be constructed with precision and attention to detail. The process was time-consuming and labor-intensive, with technicians having to extract information from a database piece-by-piece and individually plot each element. The challenge was to automate this process, reducing the time spent on menial tasks and allowing aviation experts to focus on larger conceptual issues.
About The Customer
The customer in this case study is TerraVeta, a Geospatial information firm. TerraVeta specializes in the aviation field and was looking for a solution to automate the process of creating aviation charts. These charts are essential for pilots as they provide crucial information about their destination airport or airfield. The information on these charts includes safety warnings, terrain contours, communication frequencies, geospatial data, and other instructions that affect flight safety. TerraVeta's expertise in the aviation field was crucial in the development of the automated system, as their knowledge about the intricacies and requirements of aviation charts helped shape the solution.
The Solution
The solution involved breaking the project down into two major sections: Data and Programming. The data was extracted from a government database using Alteryx, a tool that allows for quick extraction, blending, transformation, and calculation of data. The data was then cleaned and transformed into a format that could be input into the CAD plotting environment. To accommodate many users and give full control to the data engineers and mappers, the team transitioned to the open-source statistical modeling programming language, 'R'. The programming was done in MicroStation, a CAD software, with the aim of populating the chart in a natural order. The team developed a unique framework with cascading logic that mimics the computer science principle of binary search, allowing the code to be dynamic enough to anticipate unexpected rules and situations. The entire process was automated, reducing the time spent on creating aviation charts and eliminating human error.
Operational Impact
Quantitative Benefit
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