From Manual Patrols to Automated Detection: Leveraging Aerial Imagery, Computer Vision and Large Language Models for Wildfire Risk Mitigation

Abstract

Wildfire risk around electrical transmission and distribution infrastructure has grown significantly because of climate change, vegetation encroachment, prolonged drought cycles, and extreme weather events. Electric utilities are required to inspect their electric assets on regular basis. The team traditionally rely on ground patrols, helicopter inspections, and manual review of aerial photographs to evaluate asset condition and detect burn indicators. While effective, these methods are time-consuming, labor-consuming, costly, and limited by human capacity, making frequent monitoring impractical at scale. This paper presents an integrated framework that uses aerial imagery, convolutional neural networks (CNNs), computer vision (CV) segmentation, and multimodal large language models (LLMs) to automate the detection of charring, scorch marks, vegetation encroachment, and other wildfire risk factors. The approach reduces manual inspection burdens, increases monitoring frequency, saves cost, and enables proactive wildfire mitigation.

Keywords: Wildfire, Artificial Intelligence, Pre Trained Models, Computer Vision

DOI: 10.54941/ahfe1007694