Fire events threaten the safety of residents and the health of ecosystems in affected areas, and post-disaster recovery efforts also require a large investment of resources and time. In recent years, the rising frequency of fire events has motivated local governments to strengthen their monitoring and emergency response efforts. However, current fire event identification methods can only identify the presence of a fire, without the ability to distinguish its specific category. In addition, when a fire occurs, the lack of information about the affected areas makes it challenging for emergency management authorities to take timely and effective rescue measures. To address these issues, we propose a two-stage framework for fire event identification and assessment. Specifically, in the first stage, based on multi-band fused remote sensing images and heterogeneous environmental images, the proposed framework not only identifies various fire events but also accurately identifies the boundaries of the fire events. In the second stage, integrating the results of fire event identification with social media data and domain knowledge, we present a real-time assessment agent for fire events based on the large language model. This agent enables timely and accurate analysis of the impact of fires on the affected areas. We evaluate our method on a real-world authority dataset, and results show that our framework identifies fire events with an F1-score of 61.0$\%$and a mAP of 57.7$\%$, which outperforms state-of-the-art baseline methods. In addition, the assessment results of fire events in real cases indicate that the proposed fire event assessment agent can assist emergency responders in obtaining timely and accurate information.

We describe the design, deployment and testing of a module to track and graphically represent user movement in a collaborative virtual environment. This module allows for the comparison of ground-truth user/observer ratings of the affective qualities of an interaction with automatically generated representations of the participants' movements in real time. In this example, we generate three charts visible both to participants and external researchers. Two display the sum of the tracked movements of each participant, and a third displays a “synchrony visualizer”, or a correlation coefficient based on the relationship between the two participants' movements. Users and observers thus see a visual representation of “nonverbal synchrony” as it evolves over the course of the interaction. We discuss this module in the context of other applications beyond synchrony.