Machine Learning in Wildlife Biology: Algorithms, Data Issues and Availability, Workflows, Citizen Science, Code Sharing, Metadata and a Brief Historical Perspective

2018 ◽  
pp. 3-26 ◽  
Author(s):  
Grant R. W. Humphries ◽  
Falk Huettmann
Keyword(s):  
Machine Learning ◽  
Citizen Science ◽  
Historical Perspective ◽  
Code Sharing

scholarly journals Miss-identification detection in citizen science platform for biodiversity monitoring using machine learning

2020 ◽  
Vol 60 ◽  
pp. 101176
Author(s):  
Zakaria Saoud ◽  
Colin Fontaine ◽  
Grégoire Loïs ◽  
Romain Julliard ◽  
Iandry Rakotoniaina
Keyword(s):  
Machine Learning ◽  
Citizen Science ◽  
Biodiversity Monitoring

Wildfires increasingly impact rivers and water supplies

2021 ◽  
Author(s):  
Ashley Webb
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Citizen Science ◽  
Water Monitoring ◽  
Rapid Response ◽  
River Systems ◽  
Water Supplies ◽  
Changing Climate

Wildfires are increasing in frequency, severity, duration and extent with potentially catastrophic impacts on river systems and water supplies. Scientists are quantifying these impacts and studying the underlying factors driving the susceptibility of landscapes to post-fire erosion in a changing climate. Recommended actions include: (1) rapid response water monitoring during and after fires, (2) citizen science and (3) machine learning to provide communities with data and predicted impacts in near real-time.

scholarly journals Time-lapse imagery is cheap and timely in the fight against colonial species' decline

2021 ◽  
Author(s):  
Tom Hart ◽  
Fiona Jones ◽  
Caitlin Black ◽  
Chris Lintott ◽  
Casey Youngflesh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Parameter Estimation ◽  
Citizen Science ◽  
Large Scale ◽  
Individual Recognition ◽  
Spatial Scales ◽  
Time Lapse ◽  
Machine Learning Algorithms ◽  
Series Data

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.

scholarly journals A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Luca Boniardi ◽  
Federica Nobile ◽  
Massimo Stafoggia ◽  
Paola Michelozzi ◽  
Carla Ancona
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Air Pollution ◽  
Random Forest ◽  
Citizen Science ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Forest Models ◽  
Random Forest Models ◽  
The Impact

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

scholarly journals Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

2017 ◽  
Vol 34 (6) ◽  
pp. 064003 ◽  
Author(s):  
M Zevin ◽  
S Coughlin ◽  
S Bahaadini ◽  
E Besler ◽  
N Rohani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Citizen Science ◽  
Detector Characterization

Matching seismic activity with potential sources using machine Learning

2020 ◽  
Author(s):  
Gerrit Hein ◽  
Artemii Novoselov ◽  
Florian Fuchs ◽  
Götz Bokelmann
Keyword(s):  
Machine Learning ◽  
Citizen Science ◽  
Detection Algorithm ◽  
Urban Setting ◽  
Seismic Signals ◽  
Detection And Identification ◽  
Challenging Environment ◽  
Characteristic Features ◽  
Good Signal ◽  
Lower Magnitude

<p>Detecting seismic signals and identifying their origin is more and more used for understanding environmental activity. This usually depends on a good signal/noise ratio (S/N), especially for the more distant sources.</p><p>A test area for detection and identification is the urban setting of the University of Vienna, a challenging environment with more than 4000 strong-acceleration events per day. These repetitive noise events would normally classify the site as "too noisy" for any advanced earthquake research.</p><p>With the real-time open database from Wiener Linien it is possible to attribute many of the repetitive seismic signals (e.g. on a Raspberry Shake Citizen Science Station) to the surrounding trams and train lines. The detection challenge was initiated in a Citizen Science Hackathon, where public interest sparked this research. The available train schedule and more than one year of continuous seismic records is sufficient to train and test a machine learning classifier which finds most characteristic features in the signals of commuter trains and trams, such as the energy in each frequency band.</p><p>The labeled dataset can be used to train our detection algorithm to find similar signals and to help determine whether a certain signal is present or not. An additional second seismic Raspberry Shake sensor is installed in the vicinity, to further constrain the directionality of the trains.</p><p>Studying the vibrations of train signals and solving the classification task of these repetitive patterns first can help develop robust methods<br>for seismically loud environments, and might lead to the detection of lower magnitude events such as regional earthquakes or landslides. </p>

scholarly journals A system for automated analysis of subsea movies using citizen science and machine learning

2020 ◽  
Author(s):  
Victor Anton ◽  
Jannes Germishuys ◽  
Matthias Obst
Keyword(s):  
Machine Learning ◽  
Citizen Science ◽  
High Performance ◽  
Marine Protected Area ◽  
Cold Water ◽  
Image Data ◽  
Application Programming Interface ◽  
Automated Analysis ◽  
Machine Learning Algorithms ◽  
High Performance Computers

This paper describes a data system to analyse large amounts of subsea movie data for marine ecological research. The system consists of three distinct modules for data management and archiving, citizen science, and machine learning in a high performance computation environment. It allows scientists to upload underwater footage to a customised citizen science website hosted by Zooniverse, where volunteers from the public classify the footage. Classifications with high agreement among citizen scientists are then used to train machine learning algorithms. An application programming interface allows researchers to test the algorithms and track biological objects in new footage. We tested our system using recordings from remotely operated vehicles (ROVs) in a Marine Protected Area, the Kosterhavet National Park in Sweden. Results indicate a strong decline of cold-water corals in the park over a period of 15 years, showing that our system allows to effectively extract valuable occurrence and abundance data for key ecological species from underwater footage. We argue that the combination of citizen science tools, machine learning, and high performance computers are key to successfully analyse large amounts of image data in the future, suggesting that these services should be consolidated and interlinked by national and international research infrastructures. Novel information system to analyse marine underwater footage.

scholarly journals An open-source, citizen science and machine learning approach to analyse subsea movies

2021 ◽  
Vol 9 ◽  
Author(s):  
Victor Anton ◽  
Jannes Germishuys ◽  
Per Bergström ◽  
Mats Lindegarth ◽  
Matthias Obst
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Citizen Science ◽  
High Performance ◽  
Marine Protected Area ◽  
Cold Water ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Test Machine ◽  
Ecological Research

The increasing access to autonomously-operated technologies offer vast opportunities to sample large volumes of biological data. However, these technologies also impose novel demands on ecologists who need to apply tools for data management and processing that are efficient, publicly available and easy to use. Such tools are starting to be developed for a wider community and here we present an approach to combine essential analytical functions for analysing large volumes of image data in marine ecological research. This paper describes the Koster Seafloor Observatory, an open-source approach to analysing large amounts of subsea movie data for marine ecological research. The approach incorporates three distinct modules to: manage and archive the subsea movies, involve citizen scientists to accurately classify the footage and, finally, train and test machine learning algorithms for detection of biological objects. This modular approach is based on open-source code and allows researchers to customise and further develop the presented functionalities to various types of data and questions related to analysis of marine imagery. We tested our approach for monitoring cold water corals in a Marine Protected Area in Sweden using videos from remotely-operated vehicles (ROVs). Our study resulted in a machine learning model with an adequate performance, which was entirely trained with classifications provided by citizen scientists. We illustrate the application of machine learning models for automated inventories and monitoring of cold water corals. Our approach shows how citizen science can be used to effectively extract occurrence and abundance data for key ecological species and habitats from underwater footage. We conclude that the combination of open-source tools, citizen science systems, machine learning and high performance computational resources are key to successfully analyse large amounts of underwater imagery in the future.

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