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In 2020, there was a record heavy snowfall owing to climate change. Inreality, 2,000 vehicles were stuck on the highway for three days. Because ofthe freezing of the road surface, 10 vehicles had a billiard accident. Roadmanagers are required to provide indicators to alert drivers regarding snowcover at hazardous locations. This study proposes a deep learning applicationwith live image post-processing to automatically calculate a snow hazard ratioindicator. First, the road surface hidden under snow is translated using agenerative adversarial network, pix2pix. Second, snow-covered and road surfaceclasses are detected by semantic segmentation using DeepLabv3+ with MobileNetas a backbone. Based on these trained networks, we automatically compute theroad to snow rate hazard index, indicating the amount of snow covered on theroad surface. We demonstrate the applied results to 1,155 live snow images ofthe cold region in Japan. We mention the usefulness and the practicalrobustness of our study.

Road Surface Translation Under Snow-covered and Semantic Segmentation for Snow Hazard Index