This study evaluates augmented reality (AR) tools for spatial measurement in the Augmented Reality Home Assessment Tool, designed for consumer-grade mobile phones. The tool provides a reliable home assessment method based on residents’ functional limitations. A mixed-methods study with a convergent parallel design compared AR and traditional methods across speed, accuracy, ease of use, and user confidence. Learnability was tested through repeated measures with 32 participants in a within-subjects design. Results showed AR tools were faster, more accurate, easier to use, and boosted confidence, though a slight learning curve was noted. Qualitative methods, including think-aloud protocols, interviews, and observations, identified usability concerns related to information accessibility, user control, and cognitive aspects. Findings highlight the potential of phone-based AR for identifying residential barriers, supporting high-quality self-assessment for users familiar with mobile technology.

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.

Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use clauses (commonly referred to as Responsible AI Licenses) were proposed to give developers a framework for releasing AI assets while specifying their users to mitigate negative applications. As of the end of 2023, on the order of 40,000 software and model repositories have adopted responsible AI licenses licenses. Notable models licensed with behavioral use clauses include BLOOM (language) and LLaMA2 (language), Stable Diffusion (image), and GRID (robotics). This paper explores why and how these licenses have been adopted, and why and how they have been adapted to fit particular use cases. We use a mixed-methods methodology of qualitative interviews, clustering of license clauses, and quantitative analysis of license adoption. Based on this evidence we take the position that responsible AI licenses need standardization to avoid confusing users or diluting their impact. At the same time, customization of behavioral restrictions is also appropriate in some contexts (e.g., medical domains). We advocate for “standardized customization” that can meet users’ needs and can be supported via tooling.

Timely prediction of debris flow probabilities in areas impacted by wildfires is crucial to mitigate public exposure to this hazard during post-fire rainstorms. This paper presents a machine learning approach to amend an existing dataset of post-fire debris flow events with additional features reflecting existing vegetation type and geology, and train traditional and deep learning methods on a randomly selected subset of the data. The developed methods achieve AUC (area under the receiver operational characteristic curve) values of 0.93 (random forest) and 0.92 (neural network) on the test set, representing a significant improvement over a logistic regression model currently used (AUC 0.79). The paper also overviews a distributed, Kubernetesbased big data processing pipeline to efficiently retrieve features in areas impacted by new fires, and deploy the methods for real-time prediction of debris flow hazards.