Early Wildfire Detection via AI-Driven Anomaly Detection in Environmental Graph Streams: A Research Vision

Wildfires are among the most destructive and rapidly escalating natural disasters of our time. In 2024, approximately 64,897 wildfires burned nearly 8.9 million acres across the United States, and the January 2025 Los Angeles fires, driven by extreme Santa Ana wind conditions, burned over 57,000 acres, destroyed more than 16,000 structures, caused at least 31 deaths, and forced the evacuation of 200,000 residents. Traditional detection systems, whether satellite imagery or isolated sensor networks, operate reactively: they identify fires after ignition, not the environmental conditions that precede it. This paper presents a research vision for an AI-driven early wildfire detection system grounded in anomaly detection over dynamic environmental graph streams. We propose modeling wildfire-prone environments as heterogeneous graphs in which nodes represent physical entities (meteorological stations, vegetation zones, power infrastructure, and human activity points) whose edges encode their evolving spatial and causal dependencies. A graph stream anomaly detection pipeline continuously monitors this evolving structure and flags pre-ignition warning patterns before fire breaks out. The core insight is that wildfires are not random events: they are the measurable endpoint of a convergent process involving climate stress, infrastructure vulnerability, and human proximity, each of which produces detectable structural and temporal anomalies in the environmental graph hours before ignition. This work represents an interdisciplinary collaboration between AI research at ESI Algiers and climate adaptation engineering at the University of California, Davis, and constitutes, to our knowledge, the first application of dynamic graph stream anomaly detection to the wildfire early-warning problem. We present the vision, proposed framework, open research challenges, and a concrete experimental roadmap