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

人工智能的进步已经实现了令人信服的能力，超越了人类可以实现的解决方案的效率和准确性。在野生动物监测的背景下应用这些技术已被证明可以提高人工观察手段之外的生态图景，并有可能在未来取得更大的进步。由于其高营养水平，海鸟可作为海洋环境的指示物种。作为了解生态系统更广泛力量的途径，监测鸟类种群的健康状况是一种宝贵的生物资源。有许多因素可能对这些物种产生不利影响，例如近海工业、海洋污染或疾病。然而，目前收集此类数据的标准框架往往局限于人工观察。由于访问数据收集所需的远程位置存在潜在危险，因此操作的艰苦性质会产生巨大的成本。此外，由于观察者和收集到的数据之间的不一致受制于他们的观点，结果的标准化和准确性往往受到限制。与这种方法有关的后勤问题也妨碍了可能收集的数据的数量，限制了调查的广度，并导致对整个生态系统的不适当的模糊描述。由于记录间隔和准确性取决于数据收集方法的效率，通过技术手段简化过程，有机会揭开支撑可观察到的悬崖筑巢鸟动态的生态过程的神秘面纱。该项目的目标是创建、测试和改进先进的深度学习架构，这些架构将被量身定制，以扩大目前对悬崖筑巢鸟类的生态理解范围。

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.