A Novel Approach for Automatic Avalanche Detection System

Abstract

Cities worldwide are reaching operational excellence and growing smarter with the introduction of innovative technologies. These technologies, if utilized efficiently, can become assets of tomorrow. Many weather-related natural disasters have increased in frequency and severity in recent times. These natural disasters can devastate the environment, the economy, and life as we know it. Technology, if used as an instrumental asset can enhance our preparedness for emergencies. Utilizing vision-based strategies to prevent natural disasters like avalanches have been one of the significant advancements. A valuable tool for avalanche forecasting and evaluating the efficacy of avalanche prevention measures in low visibility conditions is automated snow avalanche prediction. Automation offers an easy escape from the hassle of continuous human monitoring of avalanche activity and increases detection speed and accuracy. Our solution offers an economic and precise alternative to traditional prediction systems. It takes into account different climatological factors related to snow stability to predict avalanche danger. The system analyses past and present activity to predict if avalanches can be developed. It consists of a thermal imaging camera that collects infrared images of cracked snow. These images are then subsequently examined with the help of a deep-learning image processing model to investigate the likelihood of an avalanche occurring in the crack. The prediction, if positive the authorities can be alerted and further necessary actions can be taken.

Keywords: Avalanche ,automated avalanche detection ,cracks, thermal camera, image processing, convolutional neural networks (CNN), geotagging, OTSU algorithm