912 research outputs found

    Advancing Citizen Science for Coastal and Ocean Research

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    Executive Summary Citizen Science is an approach which involves members of the public in gathering scientific data and, in more advanced cases, also involves them in the analysis of such data and in the design of scientific research. Benefits of this approach include enhancing monitoring capabilities, empowering citizens and increasing Ocean Literacy, which can itself lead to the development of environmentally-friendly behaviours. There is a long history of citizen participation in science as a general concept. However, the process of studying and understanding the best ways to develop, implement, and evaluate Citizen Science is just beginning and it has recently been proposed that the study of the process and outcomes of Citizen Science merits acknowledgement as a distinct discipline in its own right. Considering the vastness of the ocean, the extensiveness of the world’s coastlines, and the diversity of habitats, communities and species, a full scientific exploration and understanding of this realm requires intensive research and observation activities over time and space. Citizen Science is a potentially powerful tool for the generation of scientific knowledge to a level that would not be possible for the scientific community alone. Additionally, Citizen Science initiatives should be promoted because of their benefits in creating awareness of the challenges facing the world’s ocean and increasing Ocean Literacy. Responding to this, the European Marine Board convened a Working Group on Citizen Science, whose main aim was to provide new ideas and directions to further the development of Marine Citizen Science, with particular consideration for the European context. This position paper introduces the concept and rationale of Citizen Science, in particular regarding its relationship to marine research. The paper then explores European experiences of Marine Citizen Science, presenting common factors of success for European initiatives as examples of good practice. The types of data amenable to Citizen Science are outlined, along with concerns and measures relating to ensuring the scientific quality of those data. The paper further explores the social aspects of participation in Marine Citizen Science, outlining the societal benefits in terms of impact and education. The current and potential future role of technology in Marine Citizen Science projects is also addressed including, the relationship between citizens and earth observations, and the relevance of progress in the area of unmanned observing systems. The paper finally presents proposals for the improved integration and management of Marine Citizen Science on a European scale. This leads to a detailed discussion on Marine Citizen Science informing Marine Policy, taking into account the requirements of the Aarhus Convention as well as the myriad of EU marine and environmental policies. The paper concludes with the presentation of eight Strategic Action Areas for Marine Citizen Science in Europe (see summary below with details in Chapter 4). These action areas, which are aimed not only at the marine research community, but also at scientists from multiple disciplines (including non-marine), higher education institutions, funding bodies and policy makers, should together enable coherent future Europe-wide application of Marine Citizen Science for the benefit of all.publishedVersio

    Earth Observation Open Science and Innovation

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    geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

    GPU-accelerated 3D visualisation and analysis of migratory behaviour of long lived birds

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    With the amount of data we collect increasing, due to the efficacy of tagging technology improving, the methods we previously applied have begun to take longer and longer to process. As we move forward, it is important that the methods we develop also evolve with the data we collect. Maritime visualisation has already begun to leverage the power of parallel processing to accelerate visualisation. However, some of these techniques require the use of distributed computing, that while useful for datasets that contain billions of points, is harder to implement due to hardware requirements. Here we show that movement ecology can also significantly benefit from the use of parallel processing, while using GPGPU acceleration to enable the use of a single workstation. With only minor adjustments, algorithms can be implemented in parallel, enabling for computation to be completed in real time. We show this by first implementing a GPGPU accelerated visualisation of global environmental datasets. Through the use of OpenGL and CUDA, it is possible to visualise a dataset containing over 25 million datapoints per timestamp and swap between timestamps in 5ms, allowing for environmental context to be considered when visualising trajectories in real time. These can then be used alongside different GPU accelerated visualisation methods, such as aggregate flow diagrams, to explore large datasets in real time. We also continue to apply GPGPU acceleration to the analysis of migratory data through the use of parallel primitives. With these parallel primitives we show that GPGPU acceleration can allow researchers to accelerate their workflow without the need to completely understand the complexities of GPU programming, allowing for orders of magnitude faster computation times when compared to sequential CPU methods

    Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

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    This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives-for application-based opportunities, with emphasis on those that address big data with geospatial components

    CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

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    Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

    Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

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    The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

    Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

    Get PDF
    The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes
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