Exploring the utility of deep learning methodologies in the automated surveying of cliff nesting birds

探索深度学习方法在悬崖筑巢鸟类自动测量中的实用性

• 批准号: 2840623
• 金额: --
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2840623
• 关键词: Exploring; utility; deep; learning; methodologies

Advances in artificial intelligence have enabled compelling capabilities that exceed the efficiency and accuracy of solutions attainable to humans. Applying such techniques in the context of wildlife monitoring has been shown to enhance the ecological picture beyond the means of manual observation and holds the potential for even greater advancements in the future. Due to their high trophic level, seabirds may be used as an indicator species within the marine environment. As a pathway to understanding the wider strength of the ecosystem, the monitoring of the health of bird populations serves as an invaluable biological resource. There are many factors that have the potential to adversely affect these species such as offshore industry, sea pollution or disease. However, the current standard framework for the collection of such data is often limited to manual observation. With the potential dangers associated with accessing the remote locations necessary for the data collection, the strenuous nature of the operation incurs a great cost. Further, there is often restricted opportunity for the standardisation and accuracy of results due to inconsistencies between observers and data collected being constrained to their viewpoint. The logistical implications pertaining to such methods also hinder the amount of data that may be collected, limiting the breadth of the survey and resulting in an unsuitably vague picture of the overall ecosystem. With recording intervals and accuracy being dictated by the efficiency of the data collection methods, streamlining the process through technological means holds the opportunity to demystify the ecological processes underpinning the observable cliff nesting bird dynamics. The aim of this project is to create, test and improve advanced deep learning architectures that will be tailored to expand the current bounds of ecological understanding of cliff nesting birds.

人工智能的进步使得能够超出人类可实现的解决方案的效率和准确性。已显示在野生动植物监测的背景下应用此类技术，可增强生态形象，超出手动观察的手段，并在将来取得更大的进步。由于它们的营养高，海鸟可以用作海洋环境中的指标物种。作为了解生态系统更广泛强度的途径，对鸟类种群健康的监测是一种宝贵的生物学资源。有许多因素可能会对这些物种（例如海上污染或疾病）产生不利影响。但是，当前收集此类数据的标准框架通常仅限于手动观察。随着与访问数据收集所需的远程位置有关的潜在危险，操作的艰苦特性会带来巨大的成本。此外，由于观察者和收集到的数据被限制在其观点上的数据，通常存在限制性的结果。与此类方法有关的后勤含义也妨碍了可能收集的数据量，从而限制了调查的广度，并导致了整体生态系统的不合适的图片。随着记录间隔和由数据收集方法的效率决定的精度，通过技术手段简化了该过程，这使得揭开了基于可观察到的悬崖筑巢鸟动力学的生态过程的神秘化。该项目的目的是创建，测试和改善先进的深度学习体系结构，以扩大对悬崖筑巢鸟类的生态理解的当前界限。