Supporting Image Geolocation with Diagramming and Crowdsourcing
Supporting Image Geolocation with Diagramming and Crowdsourcing
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DOI：
10.1609/hcomp.v5i1.13296
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发表时间：
2017-09
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通讯作者：
Rachel Kohler;John Purviance;Kurt Luther
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文献类型：
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作者：
Rachel Kohler;John Purviance;Kurt Luther
地理定位是识别照片或视频在世界上的精确位置的过程，是许多类型调查工作的核心，从揭穿社交媒体上发布的假新闻到定位恐怖分子训练营。专业的地理定位通常是一个人工的、耗时的过程，需要在大面积的卫星图像中搜索潜在的匹配。在本文中，我们探讨了如何使用众包来支持专家图像地理定位。我们采用专家图解技术来克服新手群体的空间推理限制，使他们能够支持专家的搜索。在两个实验（n=1080）中，我们发现图表的效果明显好于地面照片，并且在任何专家干预之前，人群可以将搜索区域减少一半。我们还讨论了结合人群、专家和计算机视觉的复杂图像分析的混合方法。
Geolocation, the process of identifying the precise location in the world where a photo or video was taken, is central to many types of investigative work, from debunking fake news posted on social media to locating terrorist training camps. Professional geolocation is often a manual, time-consuming process that involves searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds, allowing them to support an expert’s search. In two experiments (n=1080), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision.