Cyclone Center: Can Citizen Scientists Improve Tropical Cyclone Intensity Records?

Hennon, Christopher C.; Knapp, Kenneth R.; Schreck, Carl J.; Stevens, Scott; Kossin, James P.; Thorne, Peter; Hennon, P.; Kruk, Michael C.; Rennie, Jared; Gadéa, Jean-Maurice; Striegl, Maximilian; Carley, Ian

doi:10.1175/bams-d-13-00152.1

Cited by 38 publications

(29 citation statements)

References 37 publications

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“…Cyclone Center, a meteorological project, asks multiple users to identify features in infrared satellite images of storms (Hennon et al. ). Each of these projects applies algorithms to aggregate the responses and produces expert‐quality data sets.…”

Section: Introductionmentioning

confidence: 99%

“…Successful citizen science projects in astrophysics, such as Galaxy Zoo (Lintott et al 2008;Willett et al 2013), Space Warps (Marshall et al 2016), Milky Way Project (Simpson et al 2012;Beaumont et al 2014), and Andromeda Project (Johnson et al 2015) rely on the judgments of multiple volunteers to classify satellite and telescope imagery. Cyclone Center, a meteorological project, asks multiple users to identify features in infrared satellite images of storms (Hennon et al 2014). Each of these projects applies algorithms to aggregate the responses and produces expert-quality data sets.…”

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confidence: 99%

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A generalized approach for producing, quantifying, and validating citizen science data from wildlife images

Swanson

Kosmala

Lintott

et al. 2016

Conservation Biology

240

299

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Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images

Swanson

Kosmala

Lintott

et al. 2016

Conservation Biology

240

299

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“…iSpot (Silvertown et al 2015), as well as data collection in the environmental sciences e.g. Zooniverse projects, such as the tropical cyclone project (Hennon et al 2015). To enable crowdsourcing of pollen identification, we implemented mechanisms for uploading unidentified pollen grain images with metadata, group-based identification and community-based competition.…”

Section: User Use Casesmentioning

confidence: 99%

The Global Pollen Project: a new tool for pollen identification and the dissemination of physical reference collections

Martin

Harvey

2017

Methods Ecol Evol

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Summary1. The study of fossil and modern pollen assemblages provides essential information about vegetation dynamics in space and time. A major methodological component of these studies is the identification of pollen grains to plant family, genus and species. This identification is achieved through the use of pollen keys and reference collections of physical specimens, which are regional in scope, disparate and incomplete, slowing the identification process. Reference material is also held in museums and research institutions, where access can be limited. Identification is particularly challenging for those new to the field, such as graduate students. 2. To aid the identification of pollen grains, and provide virtual access to reference material, we present a new online tool: the Global Pollen Project (https://globalpollenproject.org). The project aims to enable people to share and identify pollen grains and through this, will create an open, free and accessible reference library for pollen identification. 3. The online tool has been developed as an open, peer-reviewed database of global pollen, where content and expertise is crowdsourced from across the world. The tool enables: (i) the submission and identification of unknown pollen grains; (ii) the submission and digitisation of existing 'physical' reference collections and (iii) the availability of a free public database of pollen images and their metadata, for use in scientific research and education. The tool connects to external services, including the Global Biodiversity Information Facility and Neotoma Palaeoecology Database, to provide botanical descriptions and occurrence data for each taxon, alongside pollen images and metadata. 4. The database currently holds information for over 1500 species, from Europe, the Americas and Asia. As the collection grows, we envision easier pollen identification, and greater use of the database for novel research on pollen morphology and other characteristics, especially when linked to other palaeoecological databases, such as Neotoma.

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“…), there are successful projects across a variety of disciplines from meteorology (Hennon et al. ) to astronomy (Willett et al. ).…”

Section: Introductionmentioning

confidence: 99%

Economical crowdsourcing for camera trap image classification

Hsing

Bradley

Kent

et al. 2018

Remote Sens Ecol Conserv

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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 taxa, expert classifications might be preferable. We show that two types of incorrect classifications -misidentification of species and overlooking the presence of animals -have different impacts on the confidence of consensus classifications, depending on the true species pictured. Our results have implications for data capture and classification in increasingly numerous, local-scale citizen science projects. The species-specific nature of our findings suggests that the performance of crowdsourcing projects is likely to be highly sensitive to the local fauna and context. The generality of consensus algorithms will, thus, be an important consideration for ecologists interested in harnessing the power of the crowd to assist with camera trapping studies.

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Cyclone Center: Can Citizen Scientists Improve Tropical Cyclone Intensity Records?

Cited by 38 publications

References 37 publications

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images

The Global Pollen Project: a new tool for pollen identification and the dissemination of physical reference collections

Economical crowdsourcing for camera trap image classification

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