Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.

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

Paterni M.;Bini M.;Luppichini M.;Pinto L.;
2021-01-01

Abstract

Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.
2021
Andriolo, U.; Goncalves, G.; Rangel-Buitrago, N.; Paterni, M.; Bessa, F.; Goncalves, L. M. S.; Sobral, P.; Bini, M.; Duarte, D.; Fontan-Bouzas, A.; Goncalves, D.; Kataoka, T.; Luppichini, M.; Pinto, L.; Topouzelis, K.; Velez-Mendoza, A.; Merlino, S.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1116324
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