In recent years, frequent and highly destructive megafires become one of the biggest climate-induced disasters. Fire behavior models using data from many emerging sources can inform decision support tools to respond to and mitigate such megafires. Emerging edge sensing and computing technologies within the fire environment can enhance the speed, reliability, and efficiency of wildland fire management, leading to better prevention, faster response times, and more effective mitigation of fire-related disasters. However, a unified system that streamlines the integration of edge technology advances within fire science and management workflows is needed. This paper presents the design and demonstrated case studies of the WIFIRE Edge Platform that facilitates the integration of sensing and AI capabilities at the edge. The initial attack and prescribed burn concept scenarios are described, highlighting the sensor deployment and utilization at the fire front.

Wildland fire modeling tools can ingest high resolution 3D vegetation models as inputs. However, data used to build the surface fuels in these models is often at a 30-meter resolution, which does not necessarily provide sufficient detail for accurate modeling of fires. Terrestrial laser scans are increasingly being used to collect detailed vegetation data that could be integrated with new approaches to fuel and fire modeling, but manual segmentation of scans is not scalable beyond a small number of scans. There is a need to automatically segment these high resolution point clouds as they are collected in the field, such that they may be leveraged by fuel and fire models for wildland fire response and mitigation and other applied climate science. This paper summarizes our early work on a labeling, visualization and machine learning pipeline for detailed segmentation of fuels. Specific contributions are: (1) a labeling approach involving 3 dimensional segmentation of point clouds using a point cloud processing engine; (2) a visualization approach using a computer graphics engine; and (3) early results from a deep learning modeling approach for fuel segmentation by category (live and dead) and size class (1, 10, 100 and 1000 hour fuels).

Increasing social acceptance of prescribed burns is an important element of ramping up these controlled burns to the scale required to effectively mitigate destructive wildfires through reduction of excessive fire fuel loads. As part of a Design Challenge, students created concept designs for physical or virtual installations that would increase public understanding and acceptance of prescribed burns as an important tool for ending devastating megafires. The proposals defined how the public would interact with the installation and the learning goals for participants. This poster provides an overview of the virtual reality (VR) pipeline created to develop working prototypes of the immersive experiences and VR games that were proposed by the finalists in the design challenge.