Geolocating Images with Crowdsourcing and Diagramming

Geolocating Images with Crowdsourcing and Diagramming

Rachel Kohler, John Purviance, Kurt Luther

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Best Sister Conferences. Pages 5299-5303. https://doi.org/10.24963/ijcai.2018/741

Many types of investigative work involve verifying the legitimacy of visual evidence by identifying the precise geographic location where a photo or video was taken. Professional geolocation is often a manual, time-consuming process that can involve 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 so that they can support an expert's search. In an experiment (n=540), 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.
Keywords:
Humans and AI: Human Computation and Crowdsourcing
Humans and AI: Human-Computer Interaction
Computer Vision: Computer Vision