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| Gliozzo, Gianfranco; Extreme Citizen Science (ExCiteS) Research Group, University College London; Institute of Zoology, Zoological Society of London; g.gliozzo@ucl.ac.uk; Pettorelli, Nathalie; Institute of Zoology, Zoological Society of London; Nathalie.Pettorelli@ioz.ac.uk; Haklay, Mordechai (Muki); Extreme Citizen Science (ExCiteS) Research Group, University College London; m.haklay@ucl.ac.uk. |
| Within ecological research and environmental management, there is currently a focus on demonstrating the links between human well-being and wildlife conservation. Within this framework, there is a clear interest in better understanding how and why people value certain places over others. We introduce a new method that measures cultural preferences by exploring the potential of multiple online georeferenced digital photograph collections. Using ecological and social considerations, our study contributes to the detection of places that provide cultural ecosystem services. The degree of appreciation of a specific place is derived from the number of people taking and sharing pictures of it. The sequence of decisions and actions taken to share a digital picture... |
| Tipo: Peer-Reviewed Reports |
Palavras-chave: Crowdsourcing; Cultural ecosystem services; Environmental spaces detection; Online imagery; Social preferences; Spatial analysis; Volunteered geographic information (VGI). |
| Ano: 2016 |
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| Matabos, Marjolaine; Hoeberechts, Maia; Doya, Carol; Aguzzi, Jacopo; Nephin, Jessica; Reimchen, Thomas E.; Leaver, Steve; Marx, Roswitha M.; Albu, Alexandra Branzan; Fier, Ryan; Fernandez-arcaya, Ulla; Juniper, S. Kim. |
| 1.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 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). 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. 2.We compared the performance of three groups of humans and one computer vision algorithm in... |
| Tipo: Text |
Palavras-chave: Computer vision algorithms; Crowdsourcing; Deep-sea imagery; Digital Fishers; Fish counting; OceanNetworks Canada; Seafloor observatories; Underwater video. |
| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00369/47978/48006.pdf |
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