Researchers at the San Diego Supercomputer Center (SDSC) at UC San Diego have developed new deep learning models to continue improving efforts for early wildfire detection.
Researchers at the San Diego Supercomputer Center (SDSC) at UC San Diego have developed new deep learning models to continue improving efforts for early wildfire detection. These efforts at SDSC are led by the WIFIRE Lab, which is an all-hazards knowledge cyberinfrastructure developed as a management layer from data collection to modeling. Recent research experiments integrated detailed on-the-ground, real-time camera footage, satellite-based fire detections and weather data to provide a multimodal approach to the early detection of wildfires.
Led by SDSC’s Lead for Data Analytics Mai H. Nguyen and UC San Diego Computer Science & Engineering Professor Garrison W. Cottrell, the team published their work in a paper titled Multimodal Wildland Fire Smoke Detection in Remote Sensing, MDPI.
The paper discusses the team’s utilization of deep learning models, which are artificial intelligence (AI) models that use multiple processing layers to learn representations of data at increasingly complex levels of abstraction. Using these representations, the model can detect patterns that can be used to make predictions. The models specifically presented in this research include the SmokeyNet baseline model, SmokeyNet Ensemble and Multimodal SmokeyNet extension.
Read more at University of California - San Diego
Image: SmokeyNet, a deep learning model that has recently been developed in the WIFIRE Lab at SDSC, utilizes an array of data – including camera images taken atop mountains in San Diego County via the High Performance Wireless Research and Education Network (HPWREN). (Credit: HPWREN)
