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.

公民科学已经被用于各种各样的科学发现，其中许多发现是通过Zooniverse公民科学平台的技术框架和专业知识实现的。项目小组参与应用所开发的算法，以最佳地利用从天文学到动物学等学科的公民科学志愿者的投入；其中包括在自然灾害发生后对卫星和航空图像进行评估的项目，以便迅速向响者和决策者提供广泛地理区域的准确态势信息。该项目旨在通过建立最近由stfc支持的项目来推进这项工作，该项目旨在开发公民科学分类和机器学习算法的新集成。具体来说，该项目将开发和测试一个“主动学习”系统，在该系统中，机器和公民科学家实时合作，以尽快提供对基础设施损坏和生命迹象的最准确评估。这台机器首先使用之前人道主义众包部署的结果进行训练，然后在现场部署期间向人群询问进一步的信息，它认为这些信息在向受灾地区的现场响应者和决策者提供高质量数据方面最有用。这一系统将明显和立即用于未来的救灾部署。此外，发展良好的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。这些领域包括天体物理学，研究人员通过研究不断变化的天空来发现爆炸的恒星和凌日的系外行星，以及生态学和自然保护，研究人员积极研究动物迁徙和探测偷猎等人类活动。

项目成果

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

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