AEC SkylineDrone and Aircraft Detection

Drone Detection Application Commissioned by Skyline CDS, Goldmund + Wyldebeast + Wunderliebe developed the Drone Detection Application. This application enables users to combine and organize drone detection data from various detectors, creating a comprehensive overview of all drone activities.

The application offers advanced filtering capabilities, allowing users to easily sort by time, drone type, altitude, and other flight parameters. This makes it possible to perform targeted analyses and respond quickly to specific situations.

Key features:

- Combined data from multiple detectors: Integrate data from different sources for a complete overview.
- Filter options: Analyze drone activities based on flight parameters such as time, altitude, and drone type.
- Daily reports: Automatically generated reports provide daily insights into detected flights.

Drone Detection screenshot
Drone Detection screenshot
Drone Detection screenshot

The Drone Detection Application, developed by Goldmund + Wyldebeast + Wunderliebe for Skyline CDS, uses a modern tech stack that ensures performance, scalability, and efficiency. Below are the main technical components of the application:

  • FastAPI: The backend is built with FastAPI, an extremely fast web framework for Python, which provides high performance and asynchronous processing. This is essential for real-time processing of drone detection data and efficiently handling large amounts of information.
  • RabbitMQ: For communication between different services and sending drone detection notifications, RabbitMQ is used as a message broker. This ensures robust, asynchronous data transfer and smoother processing of incoming data from various detectors.
  • Next.js: The frontend is built with Next.js, providing a fast, responsive, and user-friendly interface. Thanks to server-side rendering (SSR) and static generation, the application offers not only excellent performance but also an optimal user experience.
  • PostGIS: PostGIS, a geographic extension for PostgreSQL, is used to process geospatial data. This makes it possible to accurately visualize and analyze drone flights on a map, which is essential for situational awareness in airspace monitoring.
  • TimescaleDB: The storage of time-based data is managed by TimescaleDB, an extension of PostgreSQL optimized for time-series data. This allows historical drone flight data to be efficiently stored and analyzed for trend recognition and reporting.
  • SQLAlchemy: For interaction with the database, SQLAlchemy is used as an Object-Relational Mapper (ORM). This simplifies communication between the application and the database, making the management and querying of flight data smooth. SQLAlchemy offers flexibility and makes it easier to work with complex datasets while ensuring strong integration with PostGIS and TimescaleDB.

The combination of these technologies ensures that the Drone Detection Application is robust, scalable, and efficient, with real-time processing and in-depth analysis of drone detection data.

The result

The Drone Detection Application, developed by Goldmund + Wyldebeast + Wunderliebe for Skyline CDS, provides a complete and user-friendly system for monitoring and analyzing drone activities. By combining data from multiple detectors and offering advanced filters, users gain real-time insights and can perform targeted analyses. With features such as automatic reporting, geospatial visualization via PostGIS, and fast processing with FastAPI, the application is scalable, efficient, and essential for airspace monitoring.

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