% pubman genre = article
@article{item_3596412,
title = {{Highly precise community science annotations of video camera{-}trapped fauna in challenging environments (advance online)}},
author = {Arandjelovic, Milica and Stephens, Colleen R. and Dieguez, Paula and Maldonado, Nuria and Bocksberger, Ga{\"e}lle and Despr{\'e}s{-}Einspenner, Marie{-}Lyne and Debetencourt, Benjamin and Estienne, Vittoria Luisa and Kalan, Ammie K. and McCarthy, Maureen and Granjon, Anne-C{\'e}line and St{\"a}dele, Veronika and Harder, Briana and Hacker, Lucia and Landsmann, Anja and Lynn, Laura K. and Pfund, Heidi and Ro{\v{c}}kaiov{\'a}, Zuzana and Sigler, Kristeena and Widness, Jane and Wilken, Heike and Buzharevski, Antonio and Goffe, Adeelia S. and Havercamp, Kristin and Luncz, Lydia V. and Sirianni, Giulia and Wessling, Erin G. and Wittig, Roman M. and Boesch, Christophe and K{\"u}hl, Hjalmar S.},
language = {eng},
issn = {2056-3485; 2056-3485},
doi = {10.1002/rse2.402},
year = {2024},
abstract = {{As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp{\&}See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1-min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp{\&}See community scientists. We considered 29 classification categories, including 17 species and 12 higher-level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4{\textpercent}, which increased to 98.2{\textpercent} when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring. {\copyright} 2024 The Author(s). Remote Sensing in Ecology and Conservation published by John Wiley {\&} Sons Ltd on behalf of Zoological Society of London.}},
journal = {{Remote Sensing in Ecology and Conservation}},
}