Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring.
Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring.
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DOI：
10.1016/j.patter.2020.100109
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发表时间：
2020-10-09
期刊：
影响因子：
--
通讯作者：
Connolly RM
中科院分区：
文献类型：
--
作者：
McClure EC;Sievers M;Brown CJ;Buelow CA;Ditria EM;Hayes MA;Pearson RM;Tulloch VJD;Unsworth RKF;Connolly RM
公民科学和人工智能（AI）技术在生态监测方面的发展和应用正在迅速增加。公民科学和人工智能使科学家能够创建和处理比传统方法更多的数据。然而，大型生态监测项目的管理人员对于公民科学、人工智能或两者是否最适合其资源能力和目标几乎没有指导。为了突出整合这两种技术的好处，并指导管理人员未来的实施，我们探讨了利用公民科学和人工智能进行生态监测的机遇、挑战和互补性。我们确定项目属性时，考虑实施这些技术，并建议财政资源，参与，参与者培训，技术专长，主题魅力和识别是重要的项目考虑因素。最后，我们强调，整合可以增强生态监测的成果，提高成本效益，准确性和多部门参与。公民科学和人工智能（AI）通常被孤立地用于生态监测，但它们的整合可能会对管理和科学探究产生新的好处。我们探讨了公民科学和人工智能在生态监测方面的互补性，强调了关键的机遇和挑战。我们表明，公民科学和人工智能的战略整合可以改善保护活动的结果。例如，将公民科学的公众参与利益与人工智能的先进分析能力相结合，可以增加多利益相关者在公众和科学利益问题上的雅阁。此外，与传统科学技术相比，这两种技术都加快了数据收集和处理的速度，这表明它们的整合可以快速跟踪监测和保护行动。我们提出了关键的项目属性，这将有助于项目经理优先考虑实施公民科学，人工智能或两者所需的资源。公民科学和人工智能（AI）技术在生态监测方面的发展和应用正在迅速增加。公民科学和人工智能使科学家能够创建和处理比传统方法更多的数据。然而，大型生态监测项目的管理人员对于公民科学、人工智能或两者是否最适合其资源能力和目标几乎没有指导。为了突出整合这两种技术的好处，并指导管理人员未来的实施，我们探讨了利用公民科学和人工智能进行生态监测的机遇、挑战和互补性。我们确定项目属性时，考虑实施这些技术，并建议财政资源，参与，参与者培训，技术专长，主题魅力和识别是重要的项目考虑因素。最后，我们强调，整合可以增强生态监测的成果，提高成本效益，准确性和多部门参与。
The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring is increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible with conventional methods. However, managers of large ecological monitoring projects have little guidance on whether citizen science, AI, or both, best suit their resource capacity and objectives. To highlight the benefits of integrating the two techniques and guide future implementation by managers, we explore the opportunities, challenges, and complementarities of using citizen science and AI for ecological monitoring. We identify project attributes to consider when implementing these techniques and suggest that financial resources, engagement, participant training, technical expertise, and subject charisma and identification are important project considerations. Ultimately, we highlight that integration can supercharge outcomes for ecological monitoring, enhancing cost-efficiency, accuracy, and multi-sector engagement. Citizen science and artificial intelligence (AI) are often used in isolation for ecological monitoring, but their integration likely has emergent benefits for management and scientific inquiry. We explore the complementarity of citizen science and AI for ecological monitoring, highlighting key opportunities and challenges. We show that strategic integration of citizen science and AI can improve outcomes for conservation activities. For example, coupling the public engagement benefits of citizen science with the advanced analytical capabilities of AI can increase multi-stakeholder accord on issues of public and scientific interest. Furthermore, both techniques speed up data collection and processing compared with conventional scientific techniques, suggesting that their integration can fast-track monitoring and conservation actions. We present key project attributes that will assist project managers in prioritizing the resources needed to implement citizen science, AI, or preferably both. The development and uptake of citizen science and artificial intelligence (AI) techniques for ecological monitoring are increasing rapidly. Citizen science and AI allow scientists to create and process larger volumes of data than possible with conventional methods. However, managers of large ecological monitoring projects have little guidance on whether citizen science, AI, or both, best suit their resource capacity and objectives. To highlight the benefits of integrating the two techniques and guide future implementation by managers, we explore the opportunities, challenges, and complementarities of using citizen science and AI for ecological monitoring. We identify project attributes to consider when implementing these techniques, and suggest that financial resources, engagement, participant training, technical expertise, and subject charisma and identification are important project considerations. Ultimately, we highlight that integration can supercharge outcomes for ecological monitoring, enhancing cost-efficiency, accuracy, and multi-sector engagement.
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DOI：
10.3390/s16010097
发表时间：
2016-01-14
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Gonzalez LF;Montes GA;Puig E;Johnson S;Mengersen K;Gaston KJ
通讯作者：
Gaston KJ
影响因子：
2.7
作者：
Allan, Blake M.;Nimmo, Dale G.;Ritchie, Euan G.
通讯作者：
Ritchie, Euan G.
影响因子：
5.7
作者：
Bland, Lucie M.;Orme, C. David L.;McCarthy, Michael A.
通讯作者：
McCarthy, Michael A.
影响因子：
2.7
作者：
Aide TM;Corrada-Bravo C;Campos-Cerqueira M;Milan C;Vega G;Alvarez R
通讯作者：
Alvarez R
影响因子：
6.6
作者：
Hill, Andrew P.;Prince, Peter;Rogers, Alex
通讯作者：
Rogers, Alex