[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122877-en":3,"doc-seo-122877-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122877,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection - A Call to Action","A systematic literature review examines machine learning-based methods to detect and prevent bird collisions with wind turbines. The review consolidates existing approaches, highlights critical gaps that limit current effectiveness, and frames these limitations as a basis for future research and development. The analysis emphasizes the necessity of inter- and transdisciplinary cooperation to translate technical detection progress into practical, scalable mitigation strategies. The work is positioned within broader energy transition and environmental conservation goals.","Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection:  \nA Call to Action  \nMARC PRINCIPATO1, LISA HASSELWANDER1, MICHAEL STANGNER1,  \nAND RICARDO BUETTNER1,2,(Senior Member, IEEE)  \n1Chair of Information Systems and Data Science, University of Bayreuth, 95447 Bayreuth, Germany  \n2Fraunhofer Institute for Applied Information Technology (FIT), 95444 Bayreuth, Germany Corresponding author: Ricardo Buettner ([buettner@ieee.org](buettner@ieee.org))  \nThis work was supported in part by the Deutsche Forschungsgemeinschaft (DFG), German Research Foundation under Grant 491183248; and in part by the Open Access Publishing Fund of the University of Bayreuth.  \nABSTRACT This systematic literature review explores the potential of machine learning-based approaches to detect and prevent bird collisions with wind turbines. It provides a comprehensive review of the current approaches and identifies critical gaps in the literature, which may serve as the groundwork for future research and development in this area. As a result, this work highlights the importance of inter- and transdisciplinary cooperations.  \nINDEX TERMS Energy transition, environmental conservation, wind energy, machine learning.  \nI. INTRODUCTION  \nThe climate change is one of the most pressing issues of our time and is a complex and multifaceted issue that has far-reaching impacts on our planet [1] .  \nThe burning of fossil fuels, deforestation, and other human activities are releasing large amounts of greenhouse gases into the atmosphere, causing global temperatures to rise and leading to a wide range of negative effects, including rising sea levels, more frequent and severe extreme weather events, and changes in precipitation patterns [2] . The Intergovernmental Panel on Climate Change in its fourth assessment report, stated that it is extremely likely that human activities, particularly the burning of fossil fuels, are the dominant cause of observed warming since the mid-20th century [3] .  \nThe impacts of climate change are already being felt around the world, with many regions experiencing more severe droughts, floods, heatwaves and storms. These impacts have significant economic and social costs, and threaten the well-being of both people and ecosystems [4] . The United  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was M. Shamim Kaiser  .  \nNations Framework Convention on Climate Change in the Paris Agreement, aims to limit global warming to well below 2 degrees Celsius above pre-industrial levels, and to pursue efforts to limit the temperature increase to 1.5 degrees Celsius [5].Therefore, it is important to reduce the emission of climate-damaging greenhouse gases [6] .  \nSince the energy sector is a major emitter of carbon emissions in several countries, numerous nations have set ambitious energy transition targets in order to mitigate climate change and the impact on nature [5], [7], [8] . The expansion of renewable energies, as a replacement for conventional energy production methods, is hereby envisioned to cut emissions in the energy sector drastically [9]. Wind energy, in particular, has become increasingly popular due to its ability to generate electricity without producing greenhouse gas emissions. However it requires a significant restructuring of the power sector, including large-scale construction of new wind farms and power lines [10] . The growth of wind energy development has therefore raised concerns about its impact on wildlife, particularly birds. While wind turbines offer significant environmental benefits, the negative impact they can have on bird populations cannot be ignored because the rotating  \n64026  \nThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.  \nFor more information, see [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nVOLUME 11, 2023  \nReceived 5","cbCaibxHxB2BDd66","https://ap.wps.com/l/cbCaibxHxB2BDd66","pdf",2706250,1,23,"English","en",105,"# Abstract\n# Index Terms\n# I. Introduction\n## Climate change and energy transition\n## Wildlife impacts of wind turbines\n## Existing mitigation approaches\n## Motivation for machine learning","[{\"question\":\"What problem does the document address regarding wind energy?\",\"answer\":\"It addresses the potential harm wind turbines can cause to birds, including collisions, habitat changes, and behavioral impacts, in the context of rapidly expanding wind energy development.\"},{\"question\":\"How does the document propose to improve mitigation efforts?\",\"answer\":\"It focuses on machine learning-based approaches for detecting and preventing bird collisions, aiming to provide innovative and cost-efficient solutions that balance ecological and energy objectives.\"},{\"question\":\"Why are inter- and transdisciplinary collaborations important according to the document?\",\"answer\":\"Because effective mitigation requires combining technical detection capabilities with ecological and zoological objectives, the document highlights cooperation across disciplines to bridge research gaps and enable practical deployment.\"}]","Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection - 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