Lin A Y M, Huynh Andrew, Lanckriet Gert, Barrington Luke
California Institute For Telecommunications and Information Technology, University of California San Diego, San Diego, California, United States of America.
Computer Science and Engineering Dept., University of California San Diego, San Diego, California, United States of America.
PLoS One. 2014 Dec 30;9(12):e114046. doi: 10.1371/journal.pone.0114046. eCollection 2014.
Massively parallel collaboration and emergent knowledge generation is described through a large scale survey for archaeological anomalies within ultra-high resolution earth-sensing satellite imagery. Over 10K online volunteers contributed 30K hours (3.4 years), examined 6,000 km², and generated 2.3 million feature categorizations. Motivated by the search for Genghis Khan's tomb, participants were tasked with finding an archaeological enigma that lacks any historical description of its potential visual appearance. Without a pre-existing reference for validation we turn towards consensus, defined by kernel density estimation, to pool human perception for "out of the ordinary" features across a vast landscape. This consensus served as the training mechanism within a self-evolving feedback loop between a participant and the crowd, essential driving a collective reasoning engine for anomaly detection. The resulting map led a National Geographic expedition to confirm 55 archaeological sites across a vast landscape. A increased ground-truthed accuracy was observed in those participants exposed to the peer feedback loop over those whom worked in isolation, suggesting collective reasoning can emerge within networked groups to outperform the aggregate independent ability of individuals to define the unknown.
通过对超高分辨率地球遥感卫星图像中的考古异常进行大规模调查，描述了大规模并行协作和涌现式知识生成。超过1万名在线志愿者贡献了3万小时（3.4年），检查了6000平方公里，并生成了230万个特征分类。受寻找成吉思汗陵墓的驱使，参与者的任务是寻找一个没有任何关于其潜在视觉外观历史描述的考古谜团。由于没有预先存在的验证参考，我们转向通过核密度估计定义的共识，以汇总人类对广阔景观中“异常”特征的感知。这种共识在参与者与群体之间的自我进化反馈回路中充当训练机制，这对于驱动异常检测的集体推理引擎至关重要。由此产生的地图引导了一次《国家地理》探险，在广阔的景观中确认了55个考古遗址。与单独工作的参与者相比，在那些接触到同伴反馈回路的参与者中观察到了更高的地面实况准确性，这表明集体推理可以在网络群体中出现，以超越个体汇总独立定义未知事物的能力。
