ESIP 2021 Summer Meeting Materials for the session 'Identifying technology capabilities that meet wildfire science and practitioner requirements'

From Earth Science Information Partners (ESIP)

Purpose of this page[edit | edit source]

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.

Pointers to essential documents[edit | edit source]

People involved[edit | edit source]

  • 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[edit | edit source]

What.  This session is co-organized by the Agriculture and Climate Cluster 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. Features (e.g. buildings, vegetation patches, ridgelines, etc) identified by AI/ML algorithms from UAS imagery data that are used for mitigation planning should be made discoverable for fire managers making tactical fire-fighting decisions.

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?

Agenda[edit | edit source]

  • [11 am] Workshop begins
  • Bill Teng: Introduction
  • Slido poll: Which of the following wildfire experiences apply to you?
  • [11:10 am] Wildfire problem statement, requirements, and some focus on planning by polygon
  • [12:05 pm] Slido poll: Rank the following values-at-risk that are important to *YOUR* community: from most important (rank #1) to least important (rank #6)
  • [12:10 pm] Breakouts Part 1
    • Breakout group #1: Knowledge representation for wildfire planning and execution (Focus on Polygons)
    • Breakout group #2: Technological solutions for wildfire planning and execution
  • Short break / transition (10 min)
  • [~12:45 pm] Breakouts Part 2
    • Breakout group #1: Knowledge representation for wildfire planning and execution (Focus on Values-at-Risk)
    • Breakout group #2: Technological solutions for Wildfire Planning and Execution
  • [1:10 pm] Report out from breakout groups
  • [1:20 pm] Wrap up
  • [1:30 pm] Workshop ends

Three main takeaways[edit | edit source]

  • We strongly encourage domain experts and knowledge representation experts to work together in joint sessions to derive a shared conceptualization of the problem domain that can eventually be translated into machine-readable representations to foster improved data and information interoperability.
  • There is a need to better integrate fire behavior model data, values-at-risk data, and sensor data that can be represented and visualized in a Common Operating Picture.
  • There is a need to better estimate burn severity by fusing data from various sources (in-situ, remote, model) to facilitate after-burn mitigation of possible cascading effects.