Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images

Andriolo, Umberto; Gonçalves, Gil; Rangel-Buitrago, Nelson; Paterni, Marco; Bessa, Filipa; Gonçalves, Luísa; Sobral, Paula; Bini, Mónica; Duarte, Diogo; Fontán-Bouzas, Ángela; Gonçalves, Diogo Nunes; Kataoka, Tomoya; Luppichini, Marco; Pinto, Luís; Topouzelis, Konstantinos; Vélez-Mendoza, Anubis; Merlino, Silvia

doi:10.1016/j.marpolbul.2021.112542

Cited by 38 publications

(28 citation statements)

References 52 publications

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“…The results showed an inverse relationship between the number of options the operator has to choose and the concordance level. Nevertheless, achievements were similar to the agreement among experts of a previous study [34], indicating that the skills of the CSO are low, dependent on their background and age. The agreement was comparable for all ML characteristics, namely type (0.60 vs. 0.58), material (0.75 vs. 0.76), and colour (0.69 vs. 0.65), suggesting that the interpretation of UAV images for an ML survey by CSO can also be robust, if they are properly trained.…”

Section: Citizen Science Operators Detection Performancesupporting

confidence: 85%

“…The Kendall (W) level of agreement among CSO both in the identification and classification of ML on drone images was similar to the agreement among experts [34], indicating that the skills of the CSO are low dependent on their background and age. The agreement was comparable for all ML characteristics, therefore the interpretation of UAV images for ML survey can also be robust by CSO, if properly trained.…”

Section: Discussionmentioning

confidence: 54%

“…The UAV operated automatically using the Drone Harmony (DH) ground station software. Following previous similar studies [8,34], we chose a flight height of 15 m as the right compromise between ground sampling distance (GSD) and area coverage. This setting allowed us to fly over the Test Area in 10 min.…”

Section: Study Site and Image Datasetmentioning

confidence: 99%

“…In the second step, the operator needed to label the item characteristics through a combo box-based interface composed of a hierarchical series (category, type, and material) of interdependent choices. For the aim, we slightly modified the ML shortlist proposed by the previous inter-operator reliability study [34]. The simplified list (Figure 2) included three main ML characteristics, namely type (characterised by their main potential source), material, and colour.…”

Section: Manual Image Screening and Training Frameworkmentioning

confidence: 99%

“…The consistency of MS was previously investigated by Andriolo et al [34], who evaluated the different assessment by a group of expert operators. Results pointed out that the number of items marked on images depended more on the knowledge of common items found on the site, and thus on the territoriality, than on the expertise of the operators.…”

Section: Introductionmentioning

confidence: 99%

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Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images

Merlino

Paterni

Locritani

et al. 2021

Water

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Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing this task. Students from Italian secondary schools (in this work, the CSO) were invited to identify, mark, and classify stranded litter items on a UAV orthophoto collected on an Italian beach. A specific training program and working tools were developed for the aim. The comparison with the standard in situ visual census survey returned a general underestimation (50%) of items. However, marine litter bulk categorisation was fairly in agreement with the in situ survey, especially for sources classification. The concordance level among CSO ranged between 60% and 91%, depending on the item properties considered (type, material, and colour). As the assessment accuracy was in line with previous works developed by experts, remote detection of marine litter on UAV images can be improved through citizen science programs, upon an appropriate training plan and provision of specific tools.

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Section: Citizen Science Operators Detection Performancesupporting

confidence: 85%

Section: Discussionmentioning

confidence: 54%

Section: Study Site and Image Datasetmentioning

confidence: 99%

Section: Manual Image Screening and Training Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images

Merlino

Paterni

Locritani

et al. 2021

Water

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Aerial and underwater drones for marine litter monitoring in shallow coastal waters: factors influencing item detection and cost-efficiency

Escobar-Sánchez

Markfort

Berghald

et al. 2022

Environ Monit Assess

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Although marine litter monitoring has increased over the years, the pollution of coastal waters is still understudied and there is a need for spatial and temporal data. Aerial (UAV) and underwater (ROV) drones have demonstrated their potential as monitoring tools at coastal sites; however, suitable conditions for use and cost-efficiency of the methods still need attention. This study tested UAVs and ROVs for the monitoring of floating, submerged, and seafloor items using artificial plastic plates and assessed the influence of water conditions (water transparency, color, depth, bottom substrate), item characteristics (color and size), and method settings (flight/dive height) on detection accuracy. A cost-efficiency analysis suggests that both UAV and ROV methods lie within the same cost and efficiency category as current on-boat observation and scuba diving methods and shall be considered for further testing in real scenarios for official marine litter monitoring methods.

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Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging

Rosser,

Tarpenning,

Bramante

et al. 2024

Environ Sci Pollut Res

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Aedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.

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Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images

Cited by 38 publications

References 52 publications

Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images

Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images

Aerial and underwater drones for marine litter monitoring in shallow coastal waters: factors influencing item detection and cost-efficiency

Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging

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