Using Citizen Scientists To Inform Machine Learning Algorithms To Automate The Detection Of Species In Ecological Imagery
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Using Citizen Scientists To Inform Machine Learning Algorithms To Automate The Detection Of Species In Ecological Imagery
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Author : Marshall Paul Mattingly III
language : en
Publisher:
Release Date : 2018
Using Citizen Scientists To Inform Machine Learning Algorithms To Automate The Detection Of Species In Ecological Imagery written by Marshall Paul Mattingly III and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Animal ecology categories.
The Science Of Citizen Science
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Author : Katrin Vohland
language : en
Publisher: Springer Nature
Release Date : 2021
The Science Of Citizen Science written by Katrin Vohland and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Communication categories.
This open access book discusses how the involvement of citizens into scientific endeavors is expected to contribute to solve the big challenges of our time, such as climate change and the loss of biodiversity, growing inequalities within and between societies, and the sustainability turn. The field of citizen science has been growing in recent decades. Many different stakeholders from scientists to citizens and from policy makers to environmental organisations have been involved in its practice. In addition, many scientists also study citizen science as a research approach and as a way for science and society to interact and collaborate. This book provides a representation of the practices as well as scientific and societal outcomes in different disciplines. It reflects the contribution of citizen science to societal development, education, or innovation and provides and overview of the field of actors as well as on tools and guidelines. It serves as an introduction for anyone who wants to get involved in and learn more about the science of citizen science.
Computing Communication And Learning
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Author : Sanjaya Kumar Panda
language : en
Publisher: Springer Nature
Release Date : 2023-01-01
Computing Communication And Learning written by Sanjaya Kumar Panda and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Computers categories.
This volume constitutes the refereed proceedings of the First International Conference on Computing, Communication and Learning, CoCoLe 2022, held in Warangal, India, in October 2022. The 25 full papers and 1 short paper presented were carefully reviewed and selected from 117 submissions. The CoCoLe conference focuses on three broad areas of computer science and other allied branches, namely computing, communication, and learning.
Women In Microbiology
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Author : Rachel Ann Foster
language : en
Publisher: Frontiers Media SA
Release Date : 2022-08-26
Women In Microbiology written by Rachel Ann Foster and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-26 with Science categories.
Advances In Ecoacoustics
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Author : Almo Farina
language : en
Publisher: Frontiers Media SA
Release Date : 2022-09-02
Advances In Ecoacoustics written by Almo Farina and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-02 with Science categories.
Optics And Machine Vision For Marine Observation
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Author : Hong Song
language : en
Publisher: Frontiers Media SA
Release Date : 2023-10-13
Optics And Machine Vision For Marine Observation written by Hong Song and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-13 with Science categories.
Automatic Information Extraction From Camera Trap Images Using Deep Learning
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Author : Mohammed Sadegh Norouzzadeh
language : en
Publisher:
Release Date : 2019
Automatic Information Extraction From Camera Trap Images Using Deep Learning written by Mohammed Sadegh Norouzzadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Animal ecology categories.
Our ability to study and conserve ecosystems directly depends on how much information we have about them. Motion-activated cameras also known as camera traps are cheap and non-intrusive tools to gather millions of images from wildlife. However, extracting useful information such as species, count, and the behavior of animals from the collected images is often done manually, and it is so slow and expensive that a lot of invaluable information is not extracted and thus remain untapped. This manual labor is the main roadblock in the widespread usage of camera-trap arrays. I devoted my Ph.D. dissertation to reducing the manual burden of information extraction from camera-trap images using advanced machine learning methods. For the first step, I demonstrated that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. I trained deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2-million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with over 94.9% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if my system classifies only images it is confident about, it can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers. This automation saves more than 8.4 years (i.e., over 17,000 hours at 40 hours per week) of human labeling effort on this 3.2-million-image dataset. Although I achieved outstanding results on the Snapshot Serengeti dataset, the accuracy of results highly depends on the amount, information-richness, quality, and diversity of the available data to train the models. Many camera-trap projects do not have a large, detailed set of available labeled images and hence cannot benet from my suggested machine learning techniques. In the second part of my dissertation, I combined the power of advanced machine learning algorithms and human intelligence to build a scalable, fast, and accurate active learning system to maximally reduce the amount of manual work to identify and count animals in camera-trap images. I showed that my proposed procedure could achieve more than 90.9% accuracy on the SS dataset with as little as 14,000 labels, which matches state of the art results while saving over 99.5% of human labor for labeling. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, suggesting that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.
Artificial Intelligence And Conservation
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Author : Fei Fang
language : en
Publisher: Cambridge University Press
Release Date : 2019-03-28
Artificial Intelligence And Conservation written by Fei Fang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-28 with Computers categories.
Explains how artificial intelligence methods can be used to aid conservation of wildlife, forests, coral reefs, rivers, and other natural resources.
Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification
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Author : Anil Kumar
language : en
Publisher: CRC Press
Release Date : 2020-07-19
Fuzzy Machine Learning Algorithms For Remote Sensing Image Classification written by Anil Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-19 with Computers categories.
This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels. Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications.
Phenological Research
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Author : Irene L. Hudson
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-11-24
Phenological Research written by Irene L. Hudson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-24 with Science categories.
As climate change continues to dominate the international environmental agenda, phenology – the study of the timing of recurring biological events – has received increasing research attention, leading to an emerging consensus that phenology can be viewed as an ‘early warning system’ for climate change impact. A multidisciplinary science involving many branches of ecology, geography and remote sensing, phenology to date has lacked a coherent methodological text. This new synthesis, including contributions from many of the world’s leading phenologists, therefore fills a critical gap in the current biological literature. Providing critiques of current methods, as well as detailing novel and emerging methodologies, the book, with its extensive suite of references, provides readers with an understanding of both the theoretical basis and the potential applications required to adopt and adapt new analytical and design methods. An invaluable source book for researchers and students in ecology and climate change science, the book also provides a useful reference for practitioners in a range of sectors, including human health, fisheries, forestry, agriculture and natural resource management.