This research develops an AI-based early wildfire detection system for edge device deployment in remote areas. By combining video sequence analysis with real-time environmental data (temperature, humidity, wind conditions), the approach targets early-stage smoke plume detection. The work involves implementing a multimodal deep learning architecture using a dataset of 50,000+ annotated images from 640 real wildfires across four countries. The work focuses on temporal pattern recognition to distinguish smoke from natural phenomena such as clouds and fog. The presentation will cover the research methodology, current implementation progress, and dataset characteristics. Future work will address model optimization for resource-constrained devices and communication network integration for real-time alert transmission.
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