[ESIP-all] Some AGU Fall Meeting Sessions to Consider Submitting To

Megan Carter megancarter at esipfed.org
Mon Jul 20 11:32:26 EDT 2020

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
   - 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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