Difference between revisions of "ESIP 2021 Summer Meeting Materials for the session 'Identifying technology capabilities that meet wildfire science and practitioner requirements'"

Revision as of 05:31, July 16, 2021

Purpose of this page

This page provides a summary of the session ESIP Summer 2021 meeting session 'Identifying technology capabilities that meet wildfire science and practitioner requirements' held on 2021-07-21 co-organized by the ESIP Agriculture and Climate Cluster and the ESIP Semantic Harmonization Cluster.

People involved

Session organizers:
Presenters:
- Everett Hinkley (US Forest Service, Geospatial Management Office National Remote Sensing Program Manager)
- Dave Zader (Wildland Fire Administrator for The City of Boulder, CO Fire Department (retired), Wildlife Fire Policy Committee member for the International Association of Fire Chiefs)
- Pier Buttigieg (Helmholtz Metadata Collaboration)
Session attendees: The list of attendees can be accessed from the Google Doc session notes

Overview

What. This session is co-organized by the Agriculture and Climate Cluster (ACC) and the Semantic Harmonization Cluster (hereby collectively referred to as the “Clusters”). The PDF poster on ESIP's figshare account gives you the big-picture schematic of how this session relates to data-science topics like AI/ML, semantic technology, graph database technology, etc.

Why. Environmental risks are increasingly resulting in disasters that cost the taxpayer dearly in terms of lives lost, incurred damages, and future liabilities. A recent study on the comprehensive cost of the 2018 California wildfires estimated damages at $150B and the loss of thousands of lives. In this proposed session, the Clusters will lead transdisciplinary-oriented discussions focused on both science and technology topics for managing such environmental risks. Wildfire data and information should ideally be reusable and repurposable across different fire management phases (e.g. prediction, pre-fire planning, during fire, after-fire, recovery). For example, infrastructure that is vulnerable to wildfire-induced floods identified during the active-fight fighting phase should be easily discoverable to city managers weeks or even months later, when heavy rains on burn areas may trigger catastrophic debris-flow that threaten lives. Labeled features (e.g. buildings) extracted from UAS imagery data created for community wildfire protection plans should be made discoverable for fire managers making tactical decisions on how to allocate fire-fighting resources.

How. The proposed session addresses the following question: how can we apply data and knowledge management technologies to fulfill the needs of wildfire mitigation and response?

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 '''What.  '''This session is co-organized by the Agriculture and Climate Cluster (ACC) and the Semantic Harmonization Cluster (hereby collectively referred to as the “Clusters”).  The [https://doi.org/10.6084/m9.figshare.14974266.v1 PDF poster on ESIP's figshare account] gives you the big-picture schematic of how this session relates to data-science topics like AI/ML, semantic technology, graph database technology, etc.
-'''Why.  '''Environmental risks are increasingly resulting in disasters that cost the taxpayer dearly in terms of lives lost, incurred damages, and future liabilities. A recent study on the comprehensive cost of the 2018 California wildfires estimated damages at $150B and the loss of thousands of lives. In this proposed session, the Clusters will lead transdisciplinary-oriented discussions focused on both science and technology topics for managing such environmental risks. Wildfire data and information should ideally be reusable and repurposable across different fire management phases (e.g. prediction, pre-fire planning, during fire, after-fire, recovery). For example, infrastructure that is vulnerable to wildfire-induced floods identified during the active-fight fighting phase should be easily discoverable to city managers weeks or even months later, when heavy rains on burn areas may trigger catastrophic debris-flow that threaten lives.
+'''Why.  '''Environmental risks are increasingly resulting in disasters that cost the taxpayer dearly in terms of lives lost, incurred damages, and future liabilities. A recent study on the comprehensive cost of the 2018 California wildfires estimated damages at $150B and the loss of thousands of lives. In this proposed session, the Clusters will lead transdisciplinary-oriented discussions focused on both science and technology topics for managing such environmental risks. Wildfire data and information should ideally be reusable and repurposable across different fire management phases (e.g. prediction, pre-fire planning, during fire, after-fire, recovery). For example, infrastructure that is vulnerable to wildfire-induced floods identified during the active-fight fighting phase should be easily discoverable to city managers weeks or even months later, when heavy rains on burn areas may trigger catastrophic debris-flow that threaten lives.  Labeled features (e.g. buildings) extracted from UAS imagery data created for community wildfire protection plans should be made discoverable for fire managers making tactical decisions on how to allocate fire-fighting resources.
 '''How.  '''The proposed session addresses the following question: how can we apply data and knowledge management technologies to fulfill the needs of wildfire mitigation and response?