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 spatial resolution, and detection target), they received 51,035 volunteers’ answers gathered from 281 volunteers in 3,358 tasks. In the performed experiments, it was possible to observe that the volunteers achieved satisfactory overall accuracy, higher than 75%, in the classification of forestation and non-forestation areas using the ForestEyes project. Furthermore, considering an efficient segmentation and a better image spatial resolution, they achieved almost 66% accuracy in the classification of recent deforestation, which is a great challenge to overcome. Therefore, these results show that Citizen Science might be a powerful tool in monitoring deforestation regions in rainforests as well as in obtaining high-quality labeled data.

雨林在全球生态系统中发挥着重要作用。然而，由于多种原因，其中大部分地区正面临森林砍伐和退化。创建了各种政府和私人计划，通过使用不同的方法来处理大量生成的数据，以监测和警报遥感图像中森林砍伐的增加。公民科学项目也可用于实现相同的目标。公民科学由涉及非专业志愿者的科学研究组成，他们分析、收集数据并利用他们的计算资源推动科学进步，并增加公众对特定知识领域（如天文学、化学、数学和物理学）问题的理解。从这个意义上说，这项工作提出了一个名为 ForestEyes 的公民科学项目，它通过对遥感图像的分析和分类，利用志愿者的回答来监测雨林中的森林砍伐区域。为了评估这些答案的质量，使用来自巴西合法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 PRODES 的官方真实情况进行了比较。在这项工作中，2013 年和 2016 年包含朗多尼亚州的前两个工作流程在发布后仅两个半星期内就收到了 2,050 个已创建任务中的 383 名志愿者的 35,000 多个答案。对于其他四个工作流程，即使包围相同的区域（朗多尼亚州）和不同的设置（例如，图像分割方法、图像空间分辨率和检测目标），他们也收到了 51，035 名志愿者的回答来自 281 名志愿者，参与了 3,358 项任务。在进行的实验中，可以观察到志愿者在使用 ForestEyes 项目对造林和非造林区域进行分类时取得了令人满意的总体准确率，高于 75%。此外，考虑到有效的分割和更好的图像空间分辨率，他们在近期森林砍伐的分类中达到了近 66% 的准确率，这是一个需要克服的巨大挑战。因此，这些结果表明，公民科学可能是监测热带雨林砍伐区域以及获取高质量标记数据的有力工具。使用 ForestEyes 项目对造林和非造林区域进行分类。此外，考虑到有效的分割和更好的图像空间分辨率，他们在近期森林砍伐的分类中达到了近 66% 的准确率，这是一个需要克服的巨大挑战。因此，这些结果表明，公民科学可能是监测热带雨林砍伐区域以及获取高质量标记数据的有力工具。使用 ForestEyes 项目对造林和非造林区域进行分类。此外，考虑到有效的分割和更好的图像空间分辨率，他们在近期森林砍伐的分类中达到了近 66% 的准确率，这是一个需要克服的巨大挑战。因此，这些结果表明，公民科学可能是监测热带雨林砍伐区域以及获取高质量标记数据的有力工具。