AircraftWitness: How Often Aircraft Can Locate Recordings
Authors: Kris Atallah (New York University, New York, USA)
Abstract
Aircraft overhead can reveal where an outdoor recording was made. Propellers and rotors leave Doppler curves in audio, and public ADS-B archives log equipped aircraft. We measure how often, blind and against chance. Our main test was registered and timestamped before any audio, and its two amendments came before any answer was read. It covers 24 new places, with the recorders' own clocks, a 300 s slack and a 20 km square. The best of three locators places clear-pass clips within 1 km at 4 places, where a uniform guess predicts 0.57 (p = 0.0065 after correcting for the pick). With the wrong time or swapped audio it places none. The other two hit 1 and 2. On 360 clips from 7 places of a GPS-timed network, two locators place 6% and 8% within 1 km. By place, a unit chosen after that run, they hit 4 and 5 of 7 where a uniform guess predicts 2.3. On 1,600 random public recordings, picked and cut without the truth in a time-bounded run, the three locators answer at most 20 times and place at most 2. We release AircraftWitness, a benchmark, and pass labels for 11.7 million recordings.
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Abstract
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Abstract
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Abstract
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Abstract
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Abstract
We present a content-based retrieval method for long surveillance videos both for wide-area (Airborne) as well as near-field imagery (CCTV). Our goal is to retrieve video segments, with a focus on detecting objects moving on routes, that match user-defined events of interest. The sheer size and remote locations where surveillance videos are acquired, necessitates highly compressed representations that are also meaningful for supporting user-defined queries. To address these challenges we archive long-surveillance video through lightweight processing based on low-level local spatio-temporal extraction of motion and object features. These are then hashed into an inverted index using locality-s…
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Abstract
Millions of vehicles are transported every year, tightly parked in vessels or boats. To reduce the risks of associated safety issues like fires, knowing the location of vehicles is essential, since different vehicles may need different mitigation measures, e.g. electric cars. This work is aimed at creating a solution based on a nano-drone that navigates across rows of parked vehicles and detects their license plates. We do so via a wall-following algorithm, and a CNN trained to detect license plates. All computations are done in real-time on the drone, which just sends position and detected images that allow the creation of a 2D map with the position of the plates. Our solution is capable of…
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The UAV study detects GPS spoofing in drone swarms, while AircraftWitness uses aircraft audio and public flight records to estimate where recordings were made.
Abstract
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The aerial-imagery study detects vehicles in images, whereas AircraftWitness localizes audio recordings by matching aircraft sounds with flight records.
Abstract
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Updated on 9 November 2026.