Innovative Digital Citizen Science: Active Learning for Disaster Relief

创新数字公民科学：救灾主动学习

• 批准号: BB/T018941/1
• 负责人: Brooke Simmons
• 金额: $ 2.57万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT018941%2F1
• 关键词: Innovative; Digital; Citizen; Science; Active

Citizen science has been used for a wide variety of scientific discoveries, many of which have been enabled by the technical framework and expertise of the Zooniverse citizen science platform. The project team has been involved in applying the algorithms developed to make optimal use of inputs from citizen science volunteers in disciplines ranging from astronomy to zoology; among these are projects where the crowd assesses satellite and aerial imagery following natural disasters in order to rapidly provide responders and decision-makers with accurate situational information across wide geographical areas.This project seeks to advance this work by building on a recent STFC-supported project to develop novel integrations of citizen science classifications and machine learning algorithms. Specifically, the project will develop and test an "Active Learning" system where the machine and citizen scientists collaborate in real time to provide the most accurate assessments of infrastructure damage and signs of life, as quickly as possible. The machine is first trained using the results of previous humanitarian crowdsourcing deployments, and then during a live deployment asks the crowd for further information where it deems this to be of the most use in providing high-quality data to responders and decision-makers on the ground in affected areas.This system will be of obvious and immediate use for future disaster relief deployments. Additionally, the nature of the well-developed Zooniverse platform means that the software developed here can be applied to other disciplines where researchers need to be able to accurately detect changes in data. These include astrophysics, where researchers examine the changing sky for exploding stars and transiting exoplanets, and ecology and conservation, where researchers actively study animal migration and detect human activities such as poaching.

公民科学已被用于各种科学发现，其中许多都是通过 Zooniverse 公民科学平台的技术框架和专业知识实现的。该项目团队参与了所开发的算法的应用，以充分利用从天文学到动物学等学科的公民科学志愿者的输入；其中包括群众在自然灾害发生后评估卫星和航空图像的项目，以便快速为响应者和决策者提供广泛地理区域的准确情况信息。该项目旨在通过最近 STFC 支持的项目来推进这项工作开发公民科学分类和机器学习算法的新颖集成。具体来说，该项目将开发和测试“主动学习”系统，机器和公民科学家实时协作，尽快提供对基础设施损坏和生命迹象的最准确评估。该机器首先使用之前人道主义众包部署的结果进行训练，然后在现场部署过程中要求人群提供进一步的信息，它认为这对于向救援人员和决策者提供高质量的数据最有用。该系统对于未来的救灾部署将具有明显且立即的用途。此外，成熟的 Zooniverse 平台的性质意味着这里开发的软件可以应用于研究人员需要能够准确检测数据变化的其他学科。其中包括天体物理学，研究人员研究爆炸恒星和凌日系外行星的天空变化；以及生态学和保护学，研究人员积极研究动物迁徙并探测偷猎等人类活动。

项目成果

Disaster mapping from satellites: damage detection with crowdsourced point labels

卫星灾害测绘：利用众包点标签进行损害检测

• 发表时间: 2021
• 作者: Kuzin D

Disaster, Infrastructure and Participatory Knowledge: The Planetary Response Network

灾害、基础设施和参与性知识：行星响应网络

• DOI: http://dx.10.5334/cstp.392
• 发表时间: 2022
• 期刊: Theory and Practice
• 作者: Simmons B