iNaturalist

iNaturalist

iNaturalist is an online data platform that is specifically designed to accommodate observation-type data commonly collected by citizen science programs. It encourages a community of users to agree, comment or propose names for recorded species observation. It is a platform that is utilized within studies that come from human observations, predominantly from citizen science programs such as the National Biodiversity Data Centre and iNaturalist.From: Geoinformatics for Marine and Coastal Management [2019], Quantifying the distribution and potential biotic interactions between deer and flora using species distribution modelling [2023], Shark Research [2019]

Computer Vision (CV) Technologies and Tools for Vision-based Cognitive IoT Systems

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Published in Pethuru Raj, Anupama C. Raman, Harihara Subramanian, Cognitive Internet of Things, 2022

Pethuru Raj, Anupama C. Raman, Harihara Subramanian

Computer vision is extremely beneficial for crowdsourcing programs such as iNaturalist, which is a social network of naturalists, citizen scientists, and biologists built on the concept of mapping and sharing observations of biodiversity across the globe. iNaturalist encourages people to use their mobile phones to take pictures of flora and fauna. ML classifiers and CV help the photographer to identify the species in their photograph by suggesting matches. These images are then submitted to iNaturalists where biologists can process them quickly and usefully.

Key questions on the evolution and biogeography of New Zealand alpine insects

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Published in Journal of the Royal Society of New Zealand, 2022

Thomas R. Buckley, Robert J. B. Hoare, Richard A. B. Leschen

Citizen science has a significant role to play in better understanding our alpine insects, for the reason that most visits to alpine areas are by members of the public tramping and climbing. Wider participation can be facilitated by citizen science platforms such as iNaturalist (https://www.inaturalist.org/). This kind of data sharing also leads to improved distribution data, undescribed species, and new specimens (if they have been collected under permits). These data are not perfect, one significant issue being the geographic bias towards localities that are frequented by the public (Ward 2014), and the other issue being the difficulty in identifying insect species based on one or a small number of photographs.

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Computer Vision (CV) Technologies and Tools for Vision-based Cognitive IoT Systems

Key questions on the evolution and biogeography of New Zealand alpine insects