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?
In this session, you will be invited to contribute your expertise to sketch out technical solutions that can be deployed to meet the speakers' stated needs. Your ideas will be openly accessible to individuals who may use those ideas to apply for ESIP Lab and ESIP FUNding Friday projects.
Agenda- [11 am] Workshop begins
- Introduction
- Big-picture schematic of how this session relates to data-science topics like AI/ML, semantic technology, graph database, etc.
- 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
- Everett Hinkley, US Forest Service, Geospatial Management Office National Remote Sensing Program Manager
- Wildfire Mapping--Leveraging AI/ML for needed improvements: Faster delivery, improved consistency, reduced subjectivity
- 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
- Wildfire management and planning by polygon, a tool for improved decision-making and resources usage
- Pier Buttigieg, Helmholtz Metadata Collaboration
- Representing and aligning knowledge about wildfires - the need and challenge of semantic harmonization
- [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
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