GNSS Behaviour Under Extreme Altitude Conditions: From +11,439 m (Commercial Aviation) to -30 m (Tourist Submarine, Atlantic Ocean) — Implications for Data Integrity in High-Risk AI Systems Under GDPR and Regulation (EU) 2024/1689

This study documents GNSS/GPS metadata behaviour across an unprecedentedtotal altitude range of 11,469.2 metres, combining two empiricalexperiments: (1) a commercial flight Roma Fiumicino (FCO) to BucharestOtopeni (OTP), 18 April 2026, reaching 11,439.2 m altitude at 823.5 km/h,recorded with Timestamp Camera (Samsung Galaxy A72); and (2) a touristsubmarine excursion in the Atlantic Ocean, Tenerife, 9 August 2025,descending to approximately -30 m depth, recorded with the Samsungstandard camera (562 JPG photographs). The submarine experiment reveals a previously undocumented phenomenontermed "phantom geolocation": when GPS signal is lost underwater, theSamsung standard camera silently freezes the last valid GPS fix inEXIF metadata, indistinguishable from valid data. The frozen phantomvalue of +48.5 m WGS84 corresponds to sea-level ellipsoidal height atTenerife (geoid undulation N ~ 48 m, EGM96), persisting across 243photographs over 49 minutes at approximately -30 m actual depth. NoGPSAltitudeRef indicator of below-sea-level altitude is recorded;no signal-loss tag exists in JEITA EXIF 2.32 standard. The aviation experiment confirms reliable GNSS performance at extremealtitude: all 362 photographs contain complete EXIF (latitude, longitude,altitude 11,439.2 m, speed, timestamp), enabling full georeferencingwithout internet connectivity (offline operation confirmed). A five-phase GPS altitude profile is documented for the submarineexperiment and an extended AGRI-GEO Framework (6 criteria) is proposed,adding AG-6 (explicit GNSS signal-loss disclosure) to minimum compliancerequirements for AI-assisted geographic data systems under GDPR Article5(1)(d) and EU AI Act Article 10(2)(b). The findings demonstrate that frozen GPS metadata constitutes a silentGDPR violation: inaccurate personal location data is processed withoutuser notification, creating systematic bias risk in high-risk AI systems.