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

使用高分辨率卫星数据和人工智能来量化企鹅种群的变化

• 批准号: 2648153
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2648153
• 关键词: Using; resolution; satellite; data; AI

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.

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来处理图像。该项目旨在进一步开展此类工作，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数量。还有一个目标是扩大这种方法，使其能够大规模工作，并适应于对各种物种的工作。与此同时，该项目将寻求开发一个强大的框架，将该分析以及相关的错误率和结构纳入更广泛的统计总体模型。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也很有意义，目的是更好地了解与每种数据相关的错误，并更好地了解如何约束和改进这些策略的成本效益，这些策略通常本质上不太准确。