Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ...
Technology is increasingly used in Nature Reserves and National Parks around the world to support conservation efforts. Endangered species, such as the Eurasian Lynx (Lynx lynx), are monitored by a network of automatic photo traps. Yet, this method produces vast amounts of data, which needs to be pr...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2403.02906 https://arxiv.org/abs/2403.02906 |
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ftdatacite:10.48550/arxiv.2403.02906 2024-04-28T08:41:33+00:00 Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ... Skorupska, Kinga Stryjek, Rafał Wierzbowska, Izabela Bebas, Piotr Grzeszczuk, Maciej Gago, Piotr Kowalski, Jarosław Krzywicki, Maciej Lazarek, Jagoda Kopeć, Wiesław 2024 https://dx.doi.org/10.48550/arxiv.2403.02906 https://arxiv.org/abs/2403.02906 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Human-Computer Interaction cs.HC Computer Vision and Pattern Recognition cs.CV Computers and Society cs.CY Machine Learning cs.LG FOS Computer and information sciences article Article Preprint CreativeWork 2024 ftdatacite https://doi.org/10.48550/arxiv.2403.02906 2024-04-02T10:19:34Z Technology is increasingly used in Nature Reserves and National Parks around the world to support conservation efforts. Endangered species, such as the Eurasian Lynx (Lynx lynx), are monitored by a network of automatic photo traps. Yet, this method produces vast amounts of data, which needs to be prepared, analyzed and interpreted. Therefore, researchers working in this area increasingly need support to process this incoming information. One opportunity is to seek support from volunteer Citizen Scientists who can help label the data, however, it is challenging to retain their interest. Another way is to automate the process with image recognition using convolutional neural networks. During the panel, we will discuss considerations related to nature research and conservation as well as opportunities for the use of Citizen Science and Machine Learning to expedite the process of data preparation, labelling and analysis. ... : 10 pages, 11 figures, MIDI 2023 conference ... Article in Journal/Newspaper Lynx Lynx lynx lynx DataCite Metadata Store (German National Library of Science and Technology) |
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collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
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language |
unknown |
topic |
Human-Computer Interaction cs.HC Computer Vision and Pattern Recognition cs.CV Computers and Society cs.CY Machine Learning cs.LG FOS Computer and information sciences |
spellingShingle |
Human-Computer Interaction cs.HC Computer Vision and Pattern Recognition cs.CV Computers and Society cs.CY Machine Learning cs.LG FOS Computer and information sciences Skorupska, Kinga Stryjek, Rafał Wierzbowska, Izabela Bebas, Piotr Grzeszczuk, Maciej Gago, Piotr Kowalski, Jarosław Krzywicki, Maciej Lazarek, Jagoda Kopeć, Wiesław Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ... |
topic_facet |
Human-Computer Interaction cs.HC Computer Vision and Pattern Recognition cs.CV Computers and Society cs.CY Machine Learning cs.LG FOS Computer and information sciences |
description |
Technology is increasingly used in Nature Reserves and National Parks around the world to support conservation efforts. Endangered species, such as the Eurasian Lynx (Lynx lynx), are monitored by a network of automatic photo traps. Yet, this method produces vast amounts of data, which needs to be prepared, analyzed and interpreted. Therefore, researchers working in this area increasingly need support to process this incoming information. One opportunity is to seek support from volunteer Citizen Scientists who can help label the data, however, it is challenging to retain their interest. Another way is to automate the process with image recognition using convolutional neural networks. During the panel, we will discuss considerations related to nature research and conservation as well as opportunities for the use of Citizen Science and Machine Learning to expedite the process of data preparation, labelling and analysis. ... : 10 pages, 11 figures, MIDI 2023 conference ... |
format |
Article in Journal/Newspaper |
author |
Skorupska, Kinga Stryjek, Rafał Wierzbowska, Izabela Bebas, Piotr Grzeszczuk, Maciej Gago, Piotr Kowalski, Jarosław Krzywicki, Maciej Lazarek, Jagoda Kopeć, Wiesław |
author_facet |
Skorupska, Kinga Stryjek, Rafał Wierzbowska, Izabela Bebas, Piotr Grzeszczuk, Maciej Gago, Piotr Kowalski, Jarosław Krzywicki, Maciej Lazarek, Jagoda Kopeć, Wiesław |
author_sort |
Skorupska, Kinga |
title |
Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ... |
title_short |
Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ... |
title_full |
Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ... |
title_fullStr |
Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ... |
title_full_unstemmed |
Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects ... |
title_sort |
citizen science and machine learning for research and nature conservation: the case of eurasian lynx, free-ranging rodents and insects ... |
publisher |
arXiv |
publishDate |
2024 |
url |
https://dx.doi.org/10.48550/arxiv.2403.02906 https://arxiv.org/abs/2403.02906 |
genre |
Lynx Lynx lynx lynx |
genre_facet |
Lynx Lynx lynx lynx |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2403.02906 |
_version_ |
1797571819466653696 |