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Projeto de investigação
Low-cost Unmanned Aerial Systems (UASs) for marine litter coastal mapping
Financiador
Autores
Publicações
Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
Publication . Gonçalves, Gil; Andriolo, Umberto; Gonçalves, Luisa; Sobral, Paula; Bessa, Filipa
Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.
Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images
Publication . Andriolo, Umberto; Gonçalves, Gil; Rangel-Buitrago, Nelson; Paterni, Marco; Bessa, Filipa; Gonçalves, Luisa M. S.; Sobral, Paula; Bini, Monica; Duarte, Diogo; Fontán-Bouzas, Ángela; Gonçalves, Diogo; Kataoka, Tomoya; Luppichini, Marco; Pinto, Luis; Topouzelis,Konstantinos; Vélez-Mendoza, Anubis; Merlino, Silvia
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.
Unidades organizacionais
Descrição
Palavras-chave
Marine Litter,Coastal mapping,Unmanned Aerial Systems (UASs),Low-cost, Engineering and technology
Contribuidores
Financiadores
Entidade financiadora
Fundação para a Ciência e a Tecnologia, I.P.
Programa de financiamento
Concurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2017
Número da atribuição
PTDC/EAM-REM/30324/2017