AIMC Topic: Citizen Science

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Crowdsourcing image segmentation for deep learning: integrated platform for citizen science, paid microtask, and gamification.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: Segmentation is crucial in medical imaging. Deep learning based on convolutional neural networks showed promising results. However, the absence of large-scale datasets and a high degree of inter- and intra-observer variations pose a bottl...

Deep learning with citizen science data enables estimation of species diversity and composition at continental extents.

Ecology
Effective solutions to conserve biodiversity require accurate community- and species-level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to p...

Maximizing citizen scientists' contribution to automated species recognition.

Scientific reports
Technological advances and data availability have enabled artificial intelligence-driven tools that can increasingly successfully assist in identifying species from images. Especially within citizen science, an emerging source of information filling ...

Deep learning increases the availability of organism photographs taken by citizens in citizen science programs.

Scientific reports
Citizen science programs using organism photographs have become popular, but there are two problems related to photographs. One problem is the low quality of photographs. It is laborious to identify species in photographs taken outdoors because they ...

Litter Detection with Deep Learning: A Comparative Study.

Sensors (Basel, Switzerland)
Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develo...

Deep learning identification for citizen science surveillance of tiger mosquitoes.

Scientific reports
Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Tr...

Combining citizen science and deep learning for large-scale estimation of outdoor nitrogen dioxide concentrations.

Environmental research
Reliable estimates of outdoor air pollution concentrations are needed to support global actions to improve public health. We developed a new approach to estimating annual average outdoor nitrogen dioxide (NO) concentrations using approximately 20,000...

Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics.

Scientific data
Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in c...

On the impact of Citizen Science-derived data quality on deep learning based classification in marine images.

PloS one
The evaluation of large amounts of digital image data is of growing importance for biology, including for the exploration and monitoring of marine habitats. However, only a tiny percentage of the image data collected is evaluated by marine biologists...

Comparing point counts, passive acoustic monitoring, citizen science and machine learning for bird species monitoring in the Mount Kenya ecosystem.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Biodiversity loss is a pressing challenge, with ecosystems across the world under threat from factors such as human encroachment, over exploitation and climate change. It is important to increase ecosystem monitoring efforts to provide actionable ins...