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

过去的研究表明，使用无人机、卫星或延时摄像机陷阱[1，2，3]来检测和计数鸟类是可能的，通常利用公民科学或机器学习的方法来处理图像。该项目将致力于进一步开展这项工作，利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种群。此外，还有一个目的是扩大这一方法，使之能够在规模上工作，并适应于对各种物种的工作。此外，该项目还将寻求制定一个强有力的框架，以便将这一分析以及相关的错误率和结构纳入更广泛的统计人口模型。此外，有兴趣考虑将几种形式的数据(卫星、无人机、基于Nest的视频捕获)合并为一个综合分析，目的是更好地了解与每种数据相关的误差，并更好地了解如何限制和提高这些战略的成本效益，因为这些战略本质上往往不太准确。

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.