Using high resolution satellite data and AI to quantify changes in penguin populations

过去的研究表明，可以使用无人机、卫星或延时摄像机陷阱来检测和计数鸟类[1,2,3]，通常利用公民科学或机器学习方法处理图像。该项目旨在进一步开展此类工作，使用机器学习方法对卫星或无人机数据进行实例分割，以估计南极洲的企鹅数量。此外，还有一个目标是扩展这种方法，使其能够大规模工作，并适应于各种物种的工作。除此之外，该项目还将设法制定一个强有力的框架，以便将这种分析以及有关的错误率和结构纳入更广泛的人口统计模型。此外，将几种形式的数据（卫星，无人机，基于巢的视频捕获）结合到一个综合分析中是有兴趣的，目的是更好地理解与每个相关的错误，并更好地理解如何约束和提高这些策略的成本效益，这些策略通常本质上不太准确。

Past studies have shown it is possible to detect and count birds using drone, satellite or time lapse video camera traps [1,2,3], often utilising citizen science or machine learning approaches to processing images. This project will aim to further such work, using machine learning approaches to satellite or drone data to perform instance segmentation in order to estimate penguin populations in Antarctica. Also there is an aim to expand this methodology to be able to work at scale and be adaptable to work on a variety of species. Alongside this the project will seek to develop a robust framework for incorporating that analysis, and the associated error rate and structure into a broader statistical population model. Additionally it is of interest to look at combining several forms of data (satellite, drone, nest-based video capture) into one integrated analysis, with the aim of better understanding the errors associated with each, and better understanding how to constrain and improve the more cost effective of these strategies which are often intrinsically less accurate.