BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//talk//9MVXWT BEGIN:VTIMEZONE TZID:JST BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1 TZNAME:JST TZOFFSETFROM:+0900 TZOFFSETTO:+0900 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2026-9MVXWT@talks.osgeo.org DTSTART;TZID=JST:20260902T153000 DTEND;TZID=JST:20260902T160000 DESCRIPTION:High-resolution flight trajectory data are essential for analyz ing airport operations\, environmental impacts\, and aviation safety\, yet publicly available aviation statistics typically provide only aggregated indicators such as annual movement counts and hourly traffic summaries. Su ch datasets do not preserve the individual trajectories required for spati al analysis of airport-adjacent phenomena at fine spatial and temporal sca les. As a result\, researchers and practitioners often lack accessible and reproducible methods for constructing trajectory datasets suitable for ge ospatial analysis.\n\nThis study presents a reproducible open workflow tha t converts raw ADS-B (Automatic Dependent Surveillance-Broadcast) receptio n data into analysis-ready flight trajectory datasets while explicitly exa mining uncertainties introduced throughout the processing pipeline. Implem ented with low-cost open reception infrastructure and open geospatial tool s\, the workflow is designed to be transparent\, transferable\, and applic able across airports and research contexts.\n\nThe workflow is demonstrate d using data collected in the vicinity of Narita International Airport\, o ne of the largest airports in Japan and a complex operational environment well suited to evaluating trajectory reconstruction methods. ADS-B signals broadcast by aircraft were received using a reproducible hardware configu ration consisting of a 1090 MHz antenna\, an RTL-SDR (Radio-Television Tun er Software Defined Radio) receiver\, and a Raspberry Pi 4B single-board c omputer. Signals were decoded with the open-source software dump1090-fa an d stored as records containing timestamps\, aircraft identifiers\, positio ns\, altitudes\, and speed information.\n\nThe empirical dataset covers a full year\, from 1 April 2024 to 31 March 2025\, enabling analysis across seasonal and operational variation. Initial processing stages harmonize ti mestamps\, remove records without valid positional information\, and spati ally filter the dataset to the area surrounding Narita Airport. Coordinate s are transformed into a projected system suitable for geometric operation s\, and altitude values are converted to metric units to ensure consistenc y across analytical steps.\n\nBecause ADS-B altitude values represent pres sure altitude rather than true altitude above mean sea level\, meteorologi cal observations from the Japan Meteorological Agency are incorporated to estimate corrected altitude values using the ICAO standard atmosphere rela tionship. This correction improves the interpretability of vertical trajec tory profiles and is particularly relevant for analyses that depend on acc urate vertical geometry\, including approach-path diagnostics and environm ental modeling.\n\nIndividual observations are then segmented into candida te flight trajectories based on temporal continuity between successive air craft messages. Arrival and departure movements are inferred from geometri c relationships between reconstructed trajectories and runway-end location s together with vertical trajectory characteristics. Low-altitude trajecto ry points are associated with specific runways using a minimum-distance ru le applied to runway centerline extensions. Each movement is further categ orized into operational classes defined by runway usage\, movement directi on\, and arrival or departure status.\n\nTo enrich analytical interpretati on\, aircraft attributes are integrated by linking Mode S identifiers with the OpenSky Network aircraft database\, enabling the inclusion of informa tion such as aircraft type\, manufacturer\, registration\, and nationality . Additional quality-control procedures remove trajectories with large dev iations from runway geometry\, exclude low-altitude outliers\, and filter tracks with insufficient observation density.\n\nApplying the workflow to the one-year dataset produced 245\,205 reconstructed arrival and departure trajectories associated with Narita Airport. This figure is broadly consi stent with published annual aircraft movement statistics\, suggesting that the reception and processing pipeline captures real operations at a pract ically meaningful scale. The most frequently observed aircraft types were A320\, B767-300\, B737-800\, B777-200LR\, and B787-8\, reflecting the typi cal fleet composition operating at the airport.\n\nTemporal analysis of th e reconstructed dataset indicates approximately 650 aircraft movements per day on average\, with activity concentrated primarily between 06:00 and 2 2:00. Late-night operations are comparatively limited\, a pattern relevant for interpreting time-dependent operational and environmental impacts. Th e classification results also reveal a clear asymmetry in runway usage\, w ith departures predominantly associated with Runway A and arrivals with Ru nway B\, demonstrating how reconstructed trajectories can reveal detailed operational characteristics of airport traffic.\n\nData-quality evaluation shows that the proportion of records excluded during filtering and outlie r removal remained below one percent of the total dataset\, suggesting lim ited distortion of population-level representativeness. Examination of ver tical profiles indicates that many arrival trajectories follow the expecte d approximate three-degree descent path used in instrument approaches. Nev ertheless\, some trajectory segments fall below this reference path\, with residual discrepancies on the order of approximately 100 m. These deviati ons likely reflect the combined effects of ADS-B measurement uncertainty\, reception gaps\, and the spatial limitations of meteorological pressure o bservations used in altitude correction.\n\nThe principal contribution of this study is the specification of a transparent and transferable geospati al data-engineering workflow that converts raw ADS-B reception data into c urated flight trajectory datasets suitable for spatial analysis. By explic itly documenting processing steps and identifying sources of uncertainty t hroughout the pipeline\, the workflow provides a reproducible foundation f or aviation-related geospatial research across different airports and oper ational contexts. Potential applications include airport operational analy sis\, time-of-day airspace utilization studies\, emissions-inventory prepa ration\, environmental impact assessment\, and the creation of public info rmation products describing aircraft activity. More broadly\, the study de monstrates how open reception infrastructure and open analytical tools can be integrated into reusable spatial data pipelines\, thereby contributing to transparent and reproducible geospatial practice within the FOSS4G com munity. DTSTAMP:20260717T234859Z LOCATION:Cosmos1 SUMMARY:From Raw ADS-B Signals to Reproducible Flight Trajectories: An Open Geospatial Workflow for Narita Airport - Junta Tagusari URL:https://talks.osgeo.org/foss4g-2026/talk/9MVXWT/ END:VEVENT END:VCALENDAR