[ESIP-all] Some AGU Fall Meeting Sessions to Consider Submitting To
Megan Carter
megancarter at esipfed.org
Mon Jul 27 10:22:20 EDT 2020
ESIP Community Participants suggest you consider submitting an abstract to this
updated list of 2020 AGU Fall Meeting sessions
<https://docs.google.com/document/d/1XqEdPLvbDnrezkXLgKMCRIVtREVeFgAfEGLlZvjkv18/edit?usp=sharing>.
Please feel free to add your suggestions to this list by directly editing
the document. Note: the abstract submission deadline is *this week* July
29th, 2020.
On Mon, Jul 20, 2020 at 11:32 AM Megan Carter <megancarter at esipfed.org>
wrote:
> ESIP Community Participants suggest you consider submitting an abstract to
> the following AGU Fall Meeting sessions. To avoid an overload of individual
> messages to the ESIP-All Mailing List, here is a summary of recommended
> sessions that have been shared recently:
>
> - IN028 - Linking knowledge in the Earth and Space Sciences
> <https://agu.confex.com/agu/fm20/prelim.cgi/Session/103318>: Pioneers
> of a new paradigm: The challenges confronting the Earth and Space Sciences
> (ESS) are increasingly complex, avoiding categorization or solution within
> neatly defined disciplinary boxes. Transdisciplinary, or antidisciplinary,
> approaches are required to address threats in ESS like climate change and
> space weather. However, existing approaches to integrating data and
> knowledge remain crippling to progress and collaboration. Thus, now is the
> critical time to bring together the antidisciplinary communities to create
> a new paradigm of knowledge integration. We welcome contributions that
> illustrate better structuring knowledge across projects. We will feature
> pioneering progress on three broad topics: 1) the use of data science as a
> common language between disciplines, allowing methodology transfer to
> change how we work; 2) cutting-edge approaches to knowledge management
> through knowledge graphs/networks; and 3) pioneering advances in project
> integration. This session will emerge the success stories and best
> practices for antidisciplinary projects across ESS, ushering in a new
> paradigm of linked knowledge.
> - *IN031 - Near Real-Time/Low Latency Data for Earth Science and Space
> Weather Applications
> <https://agu.confex.com/agu/fm20/prelim.cgi/Session/104084>*: Near
> real time/low latency data and new big data techniques applied to
> satellite, airborne, marine (including uninhabited aerial/marine
> systems-UxS), and surface sensors are transforming existing end-user
> applications and spawning new ones. These applications demonstrate the
> utility of timely data and advanced analyses in diverse Earth and space
> science disciplines including weather prediction, flood and river
> forecasting, earthquake hazards and tsunami forecasting, volcanic
> eruptions, natural and human-caused hazards, public health, agriculture,
> marine, early warning, and space weather applications. In addition to
> traditional and emerging computer analyses, the use of apps for smartphones
> and tablets presents an opportunity to improve and expand the timely usage
> of data products and services. This session seeks contributions that
> demonstrate the benefit of near real time/low latency scientific or social
> media data, discuss innovative real time analysis approaches including
> machine learning and big data strategies, decrease data delivery latency,
> or identify gaps in current capabilities.
> - IN037 - Scalable Cloud Optimized Spatiotemporal Data Platform (SDP)
> for Data Driven Analytics
> <https://agu.confex.com/agu/fm20/prelim.cgi/Session/105626>:
> Geospatial data has been effectively used to plan, assess, and predict the
> effects of extreme events. The diversity and complexity of geospatial data
> pose challenges in how it is stored, accessed, managed, shared, visualized,
> and fused for answering many analytical questions. Spatiotemporal Data
> Platforms (SDP) for Earth and Space Sciences are built on frameworks that
> enable efficient access, usage, and analysis of temporal sequences of
> spatial data generated by satellite, airborne and ground-based
> observations, and models. Such frameworks allow us to interoperate within
> systems of systems, scale using cloud computing or high-end computing, and
> accelerate analyses that are reusable and collaboration agnostic.SDPs also
> support efficient data-driven approaches such as Artificial Intelligence to
> extract valuable information out of large volumes of data. This session
> explores the critical components of SDPs including spatiotemporal data
> ingest, archive, storage, computing (including machine/deep learning),
> visualization, distribution, modeling, analysis ready data and GIS.
> - IN038 - Shelter in Place: Mapping Population and Infrastructure in a
> Vulnerable World
> <https://agu.confex.com/agu/fm20/prelim.cgi/Session/103648>: Understanding
> where people are located, at home or work or elsewhere—and the
> characteristics of the buildings and structures they shelter in and the
> critical systems they depend on—has never been more essential. New sources
> of data on economic and social activity, communications, population
> movement, health, and resource use, coupled with traditional sources of
> socioeconomic and infrastructure data, are being harnessed to improve near
> real-time mapping and monitoring of human behavior and vulnerability vital
> to disaster response, public health planning, climate risk assessment, and
> other applications. This session will highlight recent use of geospatial
> data on population, infrastructure, and environment in the COVID-19
> response and recovery efforts, and in other major application areas, with a
> particular focus on overlapping or interlinked hazards and on ways in which
> scientific data can be utilized more effectively in decision making.
> - *IN039 - Solving Training Data Bottleneck for Artificial
> Intelligence/Machine Learning in Earth Science
> <https://agu.confex.com/agu/fm20/prelim.cgi/Session/103091>*: While
> there are successful applications of Artificial Intelligence/Machine
> Learning (AI/ML) in Earth Science, the wider adoption of AI/ML has been
> limited. The challenge is no longer the lack of algorithms, tools, or
> computing resources, but rather the dearth of training data. Access to
> training data for supervised learning is required to attract AI/ML
> practitioners to tackle Earth Science problems. Creating labeled data at
> sufficient scales to support AI/ML algorithms is still a bottleneck and new
> strategies to increase training data size and diversity need to be
> explored. This session seeks submissions from AI/ML practitioners and data
> curators using different approaches or existing products to create new
> datasets. This session will enable the practitioners to share successful
> approaches to scale the process of generating labeled datasets. We also
> seek submissions focusing on best practices for labeling and structuring
> data including catalog and standardization to benchmark and share training
> data.
> - SY014 - Earth Intel: Open-Source Data Analytics and Tools to Address
> Eco-Security Challenges
> <https://agu.confex.com/agu/fm20/prelim.cgi/Session/103814>: Environmental
> change and variability are increasingly recognized as key drivers of
> threats to human security at local, national, regional, and global scales.
> Interdisciplinary data and analytic tools and methods are critical for
> strengthening the ability of society to improve understanding of past
> trends and interactions, monitor current conditions and key drivers,
> anticipate or predict future conditions or extremes, and assess policy
> options. Widespread access to these data and tools through open source
> approaches helps to accelerate innovation, increase the reproducibility of
> and trust in outputs, and facilitate engagement by all stakeholders. We
> welcome papers on approaches to improving the interoperability of existing
> tools and data to better support eco-security studies, case studies of the
> application of data and tools to specific eco-security problems, analyses
> of stakeholder needs for improved data and tools, and examples of academic,
> government, private sector, and civil society collaboration on eco-security
> analysis and decision making.
> - SY021 - Interactive Data Visualizations to Empower Scientific
> Projects: Improving Communication from Initial Research Development to
> Decision Support
> <https://agu.confex.com/agu/fm20/prelim.cgi/Session/104330>: The need
> for scientific data visualization became apparent during the COVID-19
> pandemic, with various visualizations popping up on a regular basis. These
> tools were created to fill a need by the general public and decision-makers
> to understand, communicate, and inform on the concerns surrounding the
> spread of the disease. Interactive data visualizations and related
> algorithms can help distinguish hidden patterns, reduce complexity, or
> raise new insights on complex scientific issues. During a project, these
> tools can improve collaborations within and among groups, advance
> communication, and provide support for decision-making. Sharing related
> experiences can benefit many scientific groups in applying these emerging
> technologies. From this session, we hope to learn from the success stories
> and increase application in more scientific projects. We welcome
> submissions from all disciplines that show their use of these tools and how
> it has benefited them, especially in interdisciplinary projects with a
> broad range of stakeholders.
>
>
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