UAS to Support Airport Safety and Operations:  Opportunities and Challenges

Hubbard, Sarah; Pak, Andrew; Gu, Yue; Jin, Yan

doi:10.1139/juvs-2016-0020

Cited by 9 publications

(6 citation statements)

References 9 publications

(11 reference statements)

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“…Many airports are also stating that they use drones themselves (or hiring drone operators to do operations for the airport), with 13.89% of airports falling within the use of drones by airport theme. Some of the use cases for drones for airports include obstruction analysis, pavement condition assessment and inspection, airfield light inspections, wildlife management, security, emergency response, and construction (Hubbard et al 2017). Indeed, we found many of these use cases mentioned as subthemes, with maintenance and wildlife control being the largest subthemes.…”

Section: Airport Policies With Regard To Dronesmentioning

confidence: 87%

“…One of the more surprising findings of this study is that the number and types of statements about drones on airport websites do not appear to vary based upon airport size. Much of the literature on the risks of drone operations near airports is focussed on large airports, such as Frankfurt, Fort Worth, and Gatwick (Hubbard et al 2017;Wendt et al 2020); however, even small airports (1-5 million passengers) in this study appear to be falling within similar themes. Thus, there is the opportunity for more research into small and medium-sized airports and the measures they are taking to manage the risks associated with drones, as well as what benefits they are seeing from their use.…”

Section: Differences Based Upon Airport Size and Locationmentioning

confidence: 98%

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Examining public-facing statements on airport websites related to aerial drones

Razak

Henderson

2022

Drone Syst. Appl.

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This study examines the public-facing statements that can be found on airport websites related to unmanned aircraft (UA). Data was extracted via manual web scraping from 288 different airports’ official websites across 69 different countries. To be selected, airports had to be one of the 100 busiest airports in terms of passenger numbers in 2017, and/or be one of the IATA slot coordinated and facilitated airports as at 10 November 2020. Manual web scraping was completed by using Google site searches for the keywords “unmanned”, “drone”, and “remotely piloted”. Phrases, sentences and paragraphs containing these keywords were collated for each airport and then thematic analysis was undertaken to identify themes within the data. Surprisingly, this study finds that 143 (49.65%) of the airports have no mention of the keywords on their websites. For those that did have statements, thematic analysis revealed 20 themes, of which the largest three were regulation, ensuring compliance, and enforcement (38.54%, 29.17%, and 25.35% of airports, respectively). There were significant differences in the number of statements overall and within specific themes based upon airport location, however, there were no statistically significant differences based upon how many passengers the airport handles.

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Section: Airport Policies With Regard To Dronesmentioning

confidence: 87%

Section: Differences Based Upon Airport Size and Locationmentioning

confidence: 98%

Examining public-facing statements on airport websites related to aerial drones

Razak

Henderson

2022

Drone Syst. Appl.

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“…As researchers collect more imagery using sUAS and build aerial imagery repositories, neural network algorithms will benefit from having a more robust set of images to provide accurate weight adjustments to the model [53][54][55]. This high level of accuracy compliments traditional wildlife surveys by accurately classifying animal species and has the potential to assist in estimating relative abundance in airport land covers [7]. Automated classification will then aid wildlife managers and airport personnel by decreasing the workload and time required to sort through large amounts of sUAS collected imagery, contributing data to strike risk assessments [6], and better informing prioritization of animal management actions to reduce animal strikes with aircraft [8][9][10][11][12][13].…”

Section: Discussionmentioning

confidence: 99%

“…Animal monitoring is routinely conducted on many airports, but bias varies among human observers, and frequent monitoring is sometimes unattainable due to time and funding constraints and the amount of area needing to be covered [5,6]. We suggest that there is opportunity to couple traditional animal survey Sensors 2021, 21, 5697 2 of 13 methodology (e.g., avian point counts) with novel animal sampling techniques to survey airports with potentially minimal bias and effort [7].…”

Section: Introductionmentioning

confidence: 99%

Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks

Zhou

Elmore

Samiappan

et al. 2021

Sensors

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In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and white-tailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals.

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“…The prairie burn sites were tested first due to the openness of these areas, which provided safe operational practice with the UAS and reduced the risk for tree, bird, and aircraft collisions [59]. Here, the "default" data collection variables were tested both before and after the prescribed burn was conducted at the Doak prairie site (Table 6).…”

Section: Prairie Burn Site Testingmentioning

confidence: 99%

Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery

Miller

Hupy

Hubbard

et al. 2022

Drones

Self Cite

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This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in natural disturbance-based management (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEOBIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the prairie burn site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed t-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining.

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UAS to Support Airport Safety and Operations: Opportunities and Challenges

Cited by 9 publications

References 9 publications

Examining public-facing statements on airport websites related to aerial drones

Examining public-facing statements on airport websites related to aerial drones

Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks

Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery

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