Geolocating Images with Crowdsourcing and Diagramming
Abstract
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.
Cite
Text
Kohler et al. "Geolocating Images with Crowdsourcing and Diagramming." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/741Markdown
[Kohler et al. "Geolocating Images with Crowdsourcing and Diagramming." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/kohler2018ijcai-geolocating/) doi:10.24963/IJCAI.2018/741BibTeX
@inproceedings{kohler2018ijcai-geolocating,
title = {{Geolocating Images with Crowdsourcing and Diagramming}},
author = {Kohler, Rachel and Purviance, John and Luther, Kurt},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5299-5303},
doi = {10.24963/IJCAI.2018/741},
url = {https://mlanthology.org/ijcai/2018/kohler2018ijcai-geolocating/}
}