Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images
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 coll...
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Veröffentlicht in: | Marine pollution bulletin 2021-08, Vol.169, p.112542-112542, Article 112542 |
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creator | 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 |
description | 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.
•Unmanned aerial systems allow categorization of macro-litter on the environment.•Sixteen researchers with different expertise marked macro-litter on drone images.•Best agreement in marking and classifying litter items among experts in litter survey•Familiarity with most common litter items (i.e., plastic) was essential in the test.•Zoom factor in screening the image is fundamental for results accuracy. |
doi_str_mv | 10.1016/j.marpolbul.2021.112542 |
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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.
•Unmanned aerial systems allow categorization of macro-litter on the environment.•Sixteen researchers with different expertise marked macro-litter on drone images.•Best agreement in marking and classifying litter items among experts in litter survey•Familiarity with most common litter items (i.e., plastic) was essential in the test.•Zoom factor in screening the image is fundamental for results accuracy.</description><identifier>ISSN: 0025-326X</identifier><identifier>EISSN: 1879-3363</identifier><identifier>DOI: 10.1016/j.marpolbul.2021.112542</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Coastal pollution ; Colour ; Drone aircraft ; Litter ; Operators ; Plastics ; Polls & surveys ; Pollution surveys ; Remote sensing ; Subgroups ; Surveying ; Surveys ; Unmanned aerial vehicle (UAV) ; Unmanned aerial vehicles ; Waste management</subject><ispartof>Marine pollution bulletin, 2021-08, Vol.169, p.112542-112542, Article 112542</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright Elsevier BV Aug 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-a4985c1066b499dfe014ffbd91aede0c3ba1dff53bbe3eddff7392fd535a2ace3</citedby><cites>FETCH-LOGICAL-c376t-a4985c1066b499dfe014ffbd91aede0c3ba1dff53bbe3eddff7392fd535a2ace3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0025326X21005762$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Andriolo, Umberto</creatorcontrib><creatorcontrib>Gonçalves, Gil</creatorcontrib><creatorcontrib>Rangel-Buitrago, Nelson</creatorcontrib><creatorcontrib>Paterni, Marco</creatorcontrib><creatorcontrib>Bessa, Filipa</creatorcontrib><creatorcontrib>Gonçalves, Luisa M.S.</creatorcontrib><creatorcontrib>Sobral, Paula</creatorcontrib><creatorcontrib>Bini, Monica</creatorcontrib><creatorcontrib>Duarte, Diogo</creatorcontrib><creatorcontrib>Fontán-Bouzas, Ángela</creatorcontrib><creatorcontrib>Gonçalves, Diogo</creatorcontrib><creatorcontrib>Kataoka, Tomoya</creatorcontrib><creatorcontrib>Luppichini, Marco</creatorcontrib><creatorcontrib>Pinto, Luis</creatorcontrib><creatorcontrib>Topouzelis, Konstantinos</creatorcontrib><creatorcontrib>Vélez-Mendoza, Anubis</creatorcontrib><creatorcontrib>Merlino, Silvia</creatorcontrib><title>Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images</title><title>Marine pollution bulletin</title><description>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.
•Unmanned aerial systems allow categorization of macro-litter on the environment.•Sixteen researchers with different expertise marked macro-litter on drone images.•Best agreement in marking and classifying litter items among experts in litter survey•Familiarity with most common litter items (i.e., plastic) was essential in the test.•Zoom factor in screening the image is fundamental for results accuracy.</description><subject>Coastal pollution</subject><subject>Colour</subject><subject>Drone aircraft</subject><subject>Litter</subject><subject>Operators</subject><subject>Plastics</subject><subject>Polls & surveys</subject><subject>Pollution surveys</subject><subject>Remote sensing</subject><subject>Subgroups</subject><subject>Surveying</subject><subject>Surveys</subject><subject>Unmanned aerial vehicle (UAV)</subject><subject>Unmanned aerial vehicles</subject><subject>Waste management</subject><issn>0025-326X</issn><issn>1879-3363</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqFkUtLBDEMx4souD4-gwUvXmbtYx6Ot8U3LHhR8FY6bbp2nWnHdlbw2xtZ8eDFU0LyS_gnf0JOOJtzxuvz9XzQaYx9t-nnggk-51xUpdghM37RtIWUtdwlM8ZEVUhRv-yTg5zXjLFGNHxGVtcpBsjUxUR7P02Q6KDH0YfVJV0E6gNWijhC0hMSJgYTk9XBAJ0gT9hHPL0hTjvQ5hUs9RMMmcZANSSve-oHvYJ8RPac7jMc_8RD8nx783R1Xywf7x6uFsvCyKaeCl22F5XhrK67sm2tA8ZL5zrbcg0WmJGd5ta5SnYdSLCYNrIVzlay0kIbkIfkbLt3TPF9gxLV4LOBvtcB4iYrgSTnDJ-C6OkfdB03KaA6pOqSN6VsWqSaLWVSzDmBU2PCk9Kn4kx9G6DW6tcA9W2A2hqAk4vtJOC9Hx6SysYDvs76BGZSNvp_d3wB3P2VRQ</recordid><startdate>202108</startdate><enddate>202108</enddate><creator>Andriolo, Umberto</creator><creator>Gonçalves, Gil</creator><creator>Rangel-Buitrago, Nelson</creator><creator>Paterni, Marco</creator><creator>Bessa, Filipa</creator><creator>Gonçalves, Luisa M.S.</creator><creator>Sobral, Paula</creator><creator>Bini, Monica</creator><creator>Duarte, Diogo</creator><creator>Fontán-Bouzas, Ángela</creator><creator>Gonçalves, Diogo</creator><creator>Kataoka, Tomoya</creator><creator>Luppichini, Marco</creator><creator>Pinto, Luis</creator><creator>Topouzelis, Konstantinos</creator><creator>Vélez-Mendoza, Anubis</creator><creator>Merlino, Silvia</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7T7</scope><scope>7TN</scope><scope>7TV</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope></search><sort><creationdate>202108</creationdate><title>Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-a4985c1066b499dfe014ffbd91aede0c3ba1dff53bbe3eddff7392fd535a2ace3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Coastal pollution</topic><topic>Colour</topic><topic>Drone aircraft</topic><topic>Litter</topic><topic>Operators</topic><topic>Plastics</topic><topic>Polls & surveys</topic><topic>Pollution surveys</topic><topic>Remote sensing</topic><topic>Subgroups</topic><topic>Surveying</topic><topic>Surveys</topic><topic>Unmanned aerial vehicle (UAV)</topic><topic>Unmanned aerial vehicles</topic><topic>Waste management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Andriolo, Umberto</creatorcontrib><creatorcontrib>Gonçalves, Gil</creatorcontrib><creatorcontrib>Rangel-Buitrago, Nelson</creatorcontrib><creatorcontrib>Paterni, Marco</creatorcontrib><creatorcontrib>Bessa, Filipa</creatorcontrib><creatorcontrib>Gonçalves, Luisa M.S.</creatorcontrib><creatorcontrib>Sobral, Paula</creatorcontrib><creatorcontrib>Bini, Monica</creatorcontrib><creatorcontrib>Duarte, Diogo</creatorcontrib><creatorcontrib>Fontán-Bouzas, Ángela</creatorcontrib><creatorcontrib>Gonçalves, Diogo</creatorcontrib><creatorcontrib>Kataoka, Tomoya</creatorcontrib><creatorcontrib>Luppichini, Marco</creatorcontrib><creatorcontrib>Pinto, Luis</creatorcontrib><creatorcontrib>Topouzelis, Konstantinos</creatorcontrib><creatorcontrib>Vélez-Mendoza, Anubis</creatorcontrib><creatorcontrib>Merlino, Silvia</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Oceanic Abstracts</collection><collection>Pollution Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Marine pollution bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Andriolo, Umberto</au><au>Gonçalves, Gil</au><au>Rangel-Buitrago, Nelson</au><au>Paterni, Marco</au><au>Bessa, Filipa</au><au>Gonçalves, Luisa M.S.</au><au>Sobral, Paula</au><au>Bini, Monica</au><au>Duarte, Diogo</au><au>Fontán-Bouzas, Ángela</au><au>Gonçalves, Diogo</au><au>Kataoka, Tomoya</au><au>Luppichini, Marco</au><au>Pinto, Luis</au><au>Topouzelis, Konstantinos</au><au>Vélez-Mendoza, Anubis</au><au>Merlino, Silvia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images</atitle><jtitle>Marine pollution bulletin</jtitle><date>2021-08</date><risdate>2021</risdate><volume>169</volume><spage>112542</spage><epage>112542</epage><pages>112542-112542</pages><artnum>112542</artnum><issn>0025-326X</issn><eissn>1879-3363</eissn><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.
•Unmanned aerial systems allow categorization of macro-litter on the environment.•Sixteen researchers with different expertise marked macro-litter on drone images.•Best agreement in marking and classifying litter items among experts in litter survey•Familiarity with most common litter items (i.e., plastic) was essential in the test.•Zoom factor in screening the image is fundamental for results accuracy.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.marpolbul.2021.112542</doi><tpages>1</tpages></addata></record> |
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subjects | Coastal pollution Colour Drone aircraft Litter Operators Plastics Polls & surveys Pollution surveys Remote sensing Subgroups Surveying Surveys Unmanned aerial vehicle (UAV) Unmanned aerial vehicles Waste management |
title | Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images |
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