Data science

Context (archaeology)

Crowdsourcing

Archaeology

Computer science

Cartography

Botany

Geography

Biology

World Wide Web

Scale (ratio)

Artificial intelligence

Citizen science

Remote Sensing in Ecology and Conservation

Camera trapping is widely used to monitor mammalian wildlife but creates large image datasets that must be classified. In response, there is a trend towards crowdsourcing image classification. For high‐profile studies of charismatic faunas, many classifications can be obtained per image, enabling consensus assessments of the image contents. For more local‐scale or less charismatic communities, however, demand may outstrip the supply of crowdsourced classifications. Here, we consider MammalWeb, a local‐scale project in North East England, which involves citizen scientists in both the capture and classification of sequences of camera trap images. We show that, for our global pool of image sequences, the probability of correct classification exceeds 99% with about nine concordant crowdsourced classifications per sequence. However, there is high variation among species. For highly recognizable species, species‐specific consensus algorithms could be even more efficient; for difficult to spot or easily confused tax...

Economical crowdsourcing for camera trap image classification