Diversifying demining: An experimental crowdsourcing method for optical mine detection


  • David Andersson

Publish date: 2009-01-08

Report number: FOI-R--2619--SE

Pages: 63

Written in: English


  • Crowdsourcing
  • diversity
  • mine detection
  • image analysis


This thesis explores the concepts of crowdsourcing and the ability of diversity, applied to optical mine detection. The idea is to use the human eye and wide and diverse workforce available on the Internet to detect mines, in addition to computer algorithms. The theory of diversity in problem solving is discussed, especially the Diversity Trumps Ability Theorem and the Diversity Prediction Theorem, and how they should be carried out for possible applications such as contrast interpretation and area reduction respectively. A simple contrast interpretation experiment is carried out comparing the results of a laymen crowd and one of experts, having the crowds examine extracts from hyperspectral images, classifying the amount of objects or mines and the type of terrain. Due to poor participation rate of the expert group, and an erroneous experiment introduction, the experiment does not yield any statistically significant results. Therefore, no conclusion is made. Experiment improvements are proposed as well as possible future applications.

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