From Raw ADS-B Signals to Reproducible Flight Trajectories: An Open Geospatial Workflow for Narita International Airport

Abstract. High-resolution flight trajectory data are essential for analysing airport operations, environmental impacts, and aviation safety, yet publicly available aviation statistics typically provide only aggregated indicators such as annual movement counts. This study presents a reproducible open workflow that converts raw ADS-B (Automatic Dependent Surveillance-Broadcast) reception data into analysis-ready flight trajectory datasets while explicitly examining uncertainties introduced throughout the processing pipeline. The workflow is implemented using low-cost open hardware and open-source analytical tools, including an RTL-SDR receiver, a Raspberry Pi single-board computer, and the R programming language. The approach is demonstrated using a full-year dataset collected near Narita International Airport (1 April 2024 to 31 March 2025), yielding 244,105 reconstructed arrival and departure trajectories broadly consistent with published annual movement statistics. The workflow covers temporal normalisation, spatial filtering, coordinate transformation, pressure altitude correction using meteorological observations, trajectory segmentation, runway classification, and quality control. By explicitly documenting each processing step and characterising residual uncertainties, the study provides a transparent and transferable foundation for aviation-related geospatial research and establishes a basis for data reuse in environmental assessment, urban planning, and open public information provision.