ForestEyes项目:公民科学家能帮助雨林吗?

F. B. J. R. Dallaqua, Á. Fazenda, F. Faria
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引用次数: 3

摘要

由于信息和通信技术(ICT)的进步,由志愿者分析、收集数据并使用他们的计算资源的科学项目,被称为“公民科学”(CS),已经变得流行起来。许多计算机科学项目被提议让公民参与不同的知识领域,如天文学、化学、数学和物理学。这项工作提出了一个名为ForestEyes的CS项目,该项目建议通过要求志愿者分析和分类遥感图像来跟踪热带雨林的砍伐情况。这些人工分类的数据被用作训练模式分类器的输入,该模式分类器将用于标记新的遥感图像。ForestEyes项目是在Zooniverse.org CS平台上创建的,为了证明志愿者回答的质量,早期活动使用了巴西合法亚马逊(BLA)的遥感图像。结果被处理并与oracle分类(PRODES -亚马逊森林砍伐监测项目)进行比较。在启动两周半后,383名志愿者(117名匿名用户和266名注册用户)收到了35000多个答案,完成了全部2050个任务。ForestEyes运动的结果表明,志愿者在遥感图像分类任务中取得了优异的有效性效果。此外,这些结果表明,CS可能是快速获得大量高质量标记数据的有力工具。
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ForestEyes Project: Can Citizen Scientists Help Rainforests?
Scientific projects involving volunteers for analyzing, collecting data, and using their computational resources, known as Citizen Science (CS), have become popular due to advances in information and communication technology (ICT). Many CS projects have been proposed to involve citizens in different knowledge domain such as astronomy, chemistry, mathematics, and physics. This work presents a CS project called ForestEyes, which proposes to track deforestation in rainforests by asking volunteers to analyze and classify remote sensing images. These manually classified data are used as input for training a pattern classifier that will be used to label new remote sensing images. ForestEyes project was created on the Zooniverse.org CS platform, and to attest the quality of the volunteers' answers, were performed early campaigns with remote sensing images from Brazilian Legal Amazon (BLA). The results were processed and compared to an oracle classification (PRODES - Amazon Deforestation Monitoring Project). Two and a half weeks after launch, more than 35,000 answers from 383 volunteers (117 anonymous and 266 registered users) were received, completing all 2050 tasks. The ForestEyes campaigns' results have shown that volunteers achieved excellent effectiveness results in remote sensing image classification task. Furthermore, these results show that CS might be a powerful tool to quickly obtain a large amount of high-quality labeled data.
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