论文标题
Foresteyes项目:构想,增强和挑战
ForestEyes Project: Conception, Enhancements, and Challenges
论文作者
论文摘要
雨林在全球生态系统中起着重要作用。但是,由于几个原因,它们的重要区域正面临森林砍伐和退化。创建了各种政府和私人计划,以监视和警报遥感图像增加森林砍伐的增加,并使用不同的方法处理显着的生成数据。公民科学项目也可以用于实现相同的目标。公民科学包括涉及非专业志愿者来分析,收集数据和使用其计算资源来结果进步的科学研究,并增加公众对天文学,化学,数学和物理学等特定知识领域中问题的理解。从这个意义上讲,这项工作提出了一个名为Foresteyes的公民科学项目,该项目通过对遥感图像的分析和分类来使用志愿者的答案来监控雨林中的森林砍伐区域。为了评估这些答案的质量,使用巴西法律亚马逊的遥感图像启动了不同的活动/工作流程,并将其结果与亚马逊森林砍伐监测项目生产的官方地面图进行了比较。在这项工作中,在2013年和2016年围绕Rondônia州包围的前两个工作流程收到了35,000美元以上的答案,从$ 383 $的志愿者中获得了$ 2,050 $的$ 2,050 $的$ 35,000 $,在他们推出后仅两周半就创建了任务。对于其他四个工作流程,甚至封闭了同一区域(Rondônia)和不同的设置(例如,图像分割方法,图像分辨率和检测目标),他们收到了$ 51,035美元的志愿者答案,从$ 281的$ 281美元的志愿者收集到$ 3,358美元的$ 3,358 $任务。在执行的实验中...
Rainforests play an important role in the global ecosystem. However, significant regions of them are facing deforestation and degradation due to several reasons. Diverse government and private initiatives were created to monitor and alert for deforestation increases from remote sensing images, using different ways to deal with the notable amount of generated data. Citizen Science projects can also be used to reach the same goal. Citizen Science consists of scientific research involving nonprofessional volunteers for analyzing, collecting data, and using their computational resources to outcome advancements in science and to increase the public's understanding of problems in specific knowledge areas such as astronomy, chemistry, mathematics, and physics. In this sense, this work presents a Citizen Science project called ForestEyes, which uses volunteer's answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests. To evaluate the quality of those answers, different campaigns/workflows were launched using remote sensing images from Brazilian Legal Amazon and their results were compared to an official groundtruth from the Amazon Deforestation Monitoring Project PRODES. In this work, the first two workflows that enclose the State of Rondônia in the years 2013 and 2016 received more than $35,000$ answers from $383$ volunteers in the $2,050$ created tasks in only two and a half weeks after their launch. For the other four workflows, even enclosing the same area (Rondônia) and different setups (e.g., image segmentation method, image resolution, and detection target), they received $51,035$ volunteers' answers gathered from $281$ volunteers in $3,358$ tasks. In the performed experiments...