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Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing?
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| Zeitschriftentitel: | Methods in Ecology and Evolution |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , , , |
| In: | Methods in Ecology and Evolution, 8, 2017, 8, S. 996-1004 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
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| Schlagwörter: |
| author_facet |
Matabos, Marjolaine Hoeberechts, Maia Doya, Carol Aguzzi, Jacopo Nephin, Jessica Reimchen, Thomas E. Leaver, Steve Marx, Roswitha M. Branzan Albu, Alexandra Fier, Ryan Fernandez‐Arcaya, Ulla Juniper, S. Kim Matabos, Marjolaine Hoeberechts, Maia Doya, Carol Aguzzi, Jacopo Nephin, Jessica Reimchen, Thomas E. Leaver, Steve Marx, Roswitha M. Branzan Albu, Alexandra Fier, Ryan Fernandez‐Arcaya, Ulla Juniper, S. Kim |
|---|---|
| author |
Matabos, Marjolaine Hoeberechts, Maia Doya, Carol Aguzzi, Jacopo Nephin, Jessica Reimchen, Thomas E. Leaver, Steve Marx, Roswitha M. Branzan Albu, Alexandra Fier, Ryan Fernandez‐Arcaya, Ulla Juniper, S. Kim |
| spellingShingle |
Matabos, Marjolaine Hoeberechts, Maia Doya, Carol Aguzzi, Jacopo Nephin, Jessica Reimchen, Thomas E. Leaver, Steve Marx, Roswitha M. Branzan Albu, Alexandra Fier, Ryan Fernandez‐Arcaya, Ulla Juniper, S. Kim Methods in Ecology and Evolution Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? Ecological Modeling Ecology, Evolution, Behavior and Systematics |
| author_sort |
matabos, marjolaine |
| spelling |
Matabos, Marjolaine Hoeberechts, Maia Doya, Carol Aguzzi, Jacopo Nephin, Jessica Reimchen, Thomas E. Leaver, Steve Marx, Roswitha M. Branzan Albu, Alexandra Fier, Ryan Fernandez‐Arcaya, Ulla Juniper, S. Kim 2041-210X 2041-210X Wiley Ecological Modeling Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1111/2041-210x.12746 <jats:title>Summary</jats:title><jats:p> <jats:list> <jats:list-item><jats:p>Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90 000 h of video data from seafloor cameras and remotely operated vehicles. Manual processing of these data is time‐consuming and highly labour‐intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea.</jats:p></jats:list-item> <jats:list-item><jats:p>We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) <jats:italic>Anoplopoma fimbria</jats:italic>, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited <jats:italic>via</jats:italic> a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert.</jats:p></jats:list-item> <jats:list-item><jats:p>All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest.</jats:p></jats:list-item> <jats:list-item><jats:p>As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep‐sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development.</jats:p></jats:list-item> </jats:list> </jats:p> Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? Methods in Ecology and Evolution |
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Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
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Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
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Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
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Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
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expert, crowd, students or algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
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Ecological Modeling Ecology, Evolution, Behavior and Systematics |
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http://dx.doi.org/10.1111/2041-210x.12746 |
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2017 |
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996-1004 |
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<jats:title>Summary</jats:title><jats:p>
<jats:list>
<jats:list-item><jats:p>Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90 000 h of video data from seafloor cameras and remotely operated vehicles. Manual processing of these data is time‐consuming and highly labour‐intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea.</jats:p></jats:list-item>
<jats:list-item><jats:p>We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) <jats:italic>Anoplopoma fimbria</jats:italic>, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited <jats:italic>via</jats:italic> a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert.</jats:p></jats:list-item>
<jats:list-item><jats:p>All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest.</jats:p></jats:list-item>
<jats:list-item><jats:p>As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep‐sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development.</jats:p></jats:list-item>
</jats:list>
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| author | Matabos, Marjolaine, Hoeberechts, Maia, Doya, Carol, Aguzzi, Jacopo, Nephin, Jessica, Reimchen, Thomas E., Leaver, Steve, Marx, Roswitha M., Branzan Albu, Alexandra, Fier, Ryan, Fernandez‐Arcaya, Ulla, Juniper, S. Kim |
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| description | <jats:title>Summary</jats:title><jats:p> <jats:list> <jats:list-item><jats:p>Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90 000 h of video data from seafloor cameras and remotely operated vehicles. Manual processing of these data is time‐consuming and highly labour‐intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea.</jats:p></jats:list-item> <jats:list-item><jats:p>We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) <jats:italic>Anoplopoma fimbria</jats:italic>, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited <jats:italic>via</jats:italic> a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert.</jats:p></jats:list-item> <jats:list-item><jats:p>All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest.</jats:p></jats:list-item> <jats:list-item><jats:p>As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep‐sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development.</jats:p></jats:list-item> </jats:list> </jats:p> |
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| spelling | Matabos, Marjolaine Hoeberechts, Maia Doya, Carol Aguzzi, Jacopo Nephin, Jessica Reimchen, Thomas E. Leaver, Steve Marx, Roswitha M. Branzan Albu, Alexandra Fier, Ryan Fernandez‐Arcaya, Ulla Juniper, S. Kim 2041-210X 2041-210X Wiley Ecological Modeling Ecology, Evolution, Behavior and Systematics http://dx.doi.org/10.1111/2041-210x.12746 <jats:title>Summary</jats:title><jats:p> <jats:list> <jats:list-item><jats:p>Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90 000 h of video data from seafloor cameras and remotely operated vehicles. Manual processing of these data is time‐consuming and highly labour‐intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea.</jats:p></jats:list-item> <jats:list-item><jats:p>We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) <jats:italic>Anoplopoma fimbria</jats:italic>, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited <jats:italic>via</jats:italic> a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert.</jats:p></jats:list-item> <jats:list-item><jats:p>All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest.</jats:p></jats:list-item> <jats:list-item><jats:p>As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep‐sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development.</jats:p></jats:list-item> </jats:list> </jats:p> Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? Methods in Ecology and Evolution |
| spellingShingle | Matabos, Marjolaine, Hoeberechts, Maia, Doya, Carol, Aguzzi, Jacopo, Nephin, Jessica, Reimchen, Thomas E., Leaver, Steve, Marx, Roswitha M., Branzan Albu, Alexandra, Fier, Ryan, Fernandez‐Arcaya, Ulla, Juniper, S. Kim, Methods in Ecology and Evolution, Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing?, Ecological Modeling, Ecology, Evolution, Behavior and Systematics |
| title | Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
| title_full | Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
| title_fullStr | Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
| title_full_unstemmed | Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
| title_short | Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
| title_sort | expert, crowd, students or algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
| title_unstemmed | Expert, Crowd, Students or Algorithm: who holds the key to deep‐sea imagery ‘big data’ processing? |
| topic | Ecological Modeling, Ecology, Evolution, Behavior and Systematics |
| url | http://dx.doi.org/10.1111/2041-210x.12746 |