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Record W4416819379 · doi:10.61618/kvwx6292

Letter To the Editor: Public Drone Use and Its Impact on Search and Rescue and Wildfire Operations

2025· article· W4416819379 on OpenAlexaboutno aff
Tammy Meredith, Elizabeth Cuevas

Bibliographic record

VenueThe Journal of Search and Rescue · 2025
Typearticle
Language
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsDroneSearch and rescueService (business)AviationAviation safetyPublic servicePoison controlVisitor pattern

Abstract

fetched live from OpenAlex

Public use of unmanned aircraft or drones continues to affect emergency response operations in ways that responders, aviation teams, and agencies cannot ignore. Recreational drone ownership has expanded rapidly, and more individuals are flying these devices near active disaster scenes. Although many operators believe they are assisting, uncoordinated drone flights introduce risks that slow operations and place both responders and survivors in danger. As researchers and practitioners working in disaster operations, we have observed a significant rise in uncoordinated drone activity at active response sites. This letter aims to highlight that trend and call on response agencies, regulators, drone manufacturers, and the public to address this issue directly. The July 2025 floods in the Texas Hill Country demonstrate how quickly these hazards can develop. As helicopter crews conducted hoist operations and reconnaissance in unstable conditions, unauthorized drones entered the airspace. Several near misses were reported, and one rescue helicopter in Kerr County was struck by a drone and forced to land, removing a vital aircraft from service during an active rescue cycle (KSAT News, 2025). Responders noted that unauthorized drones complicated flight paths and reduced available decision time during aerial search operations (DroneLife, 2025; Stokel-Walker, 2025). Similar interference has been documented internationally. In January 2025 a privately operated drone collided with a Super Scooper aircraft working an active wildfire in California, causing damage significant enough to remove the aircraft from service during suppression efforts (Los Angeles Times, 2025). Unmanned aircraft sightings reported by aircrews indicate a steady increase in unsafe operations, with pilots reporting evasive maneuvers in nearly three percent of encounters in 2025 (Wallace, 2025). The risks of drone collisions with helicopters or fixed wing aircraft are well established. Federal Aviation Administration impact testing shows that even lightweight drones can damage rotors, engines, or windshields upon impact (Federal Aviation Administration, 2017). When pilots see or suspect a drone in their airspace, they must slow, alter, or temporarily suspend flight operations until the area is confirmed safe, which introduces complex operational risks. These delays also reduce the speed and effectiveness of rescues and wildfire suppression. Although many recreational operators intend to help, uncoordinated drone flights do not support responders. Incident commanders cannot verify or integrate imagery or data collected by personal drones, and such information may conflict with operational formats or create liability concerns. During Hurricane Harvey, unauthorized drones initially interfered with air operations, prompting the Texas Military Department to warn publicly that “civilian drones pose EXTREME risks to our rescue pilots and crews in high need areas” (IoT World Today, 2017). After this warning, several volunteer drone groups worked with agencies to coordinate flight patterns, ground aircraft upon request, and share imagery only through official channels. This shift improved safety and demonstrated that civilian groups can contribute meaningfully when they operate under unified direction rather than independently. The DroneUp partnerships used during Harvey further illustrate how civilian operators, when organized and aligned with official command, can support search efforts without adding risk (AirSight, 2017). That experience underscores the difference between organized support and self-directed flight. Legal restrictions prohibiting drone operation near disaster scenes exist in the United States, the United Kingdom, Canada, and Australia. Enforcement remains challenging. Responders cannot divert personnel to locate drone operators during an active emergency, and counter-drone technologies are not universally available or authorized for local agencies. Despite these concerns, drones have meaningful value when deployed within coordinated response systems. Agencies in the United Kingdom, Canada, Australia, and the United States use drones to map fire behavior, document search areas, assess structural conditions, and deliver real-time situational awareness. Research from Sweden shows that automated external defibrillator-equipped drones arrived before ambulances in a majority of trials and reduced time to first shock by nearly two minutes, demonstrating the potential of well-integrated drone systems to save lives (Karolinska Institute, 2020). Emergency response agencies should strengthen public education that emphasizes how unauthorized drone flights restrict aviation safety. The West Midlands Fire Service has set a strong example with its direct messaging urging the public to keep personal drones away from emergency scenes (West Midlands Fire Service, 2023). Regulators and manufacturers should expand geofencing and develop automatic restrictions that activate during declared emergencies. Agencies should also create accredited pathways that allow trained civilian pilots to support operations safely rather than through uncoordinated flights. Uncoordinated drone flights place responders and communities at unnecessary risk. As severe flooding, drought conditions, and wildfire activity increase across multiple regions, safe and predictable airspace will remain essential to effective emergency aviation. Keeping uncoordinated, personal drones grounded during active incidents is a necessary and achievable step toward protecting both responders and the people they are working to reach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0140.015
Insufficient payload (model declined to judge)0.0090.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.289
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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