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

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

Rain Code: Multi-Frame Based Forecasting Spatiotemporal Precipitation Using ConvLSTM

Recently, flood damage has become a social problem owing to unexperienced weather conditions arising from climate change. An immediate response to heavy rain and high water levels is important for the mitigation of casualties and economic losses and also for rapid recovery. Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 hours forward for flood damage mitigation. This paper proposes a rain-code approach for spatiotemporal precipitation forecasting. We propose a novel rainy feature that represents a temporal rainy process using multi-frame fusion for timestep reduction. We perform rain-code studies with various term ranges based on spatiotemporal precipitation forecasting using the standard ConvLSTM. We applied to a dam region within the Japanese rainy term hourly precipitation data, under 2006 to 2019 approximately 127 thousands hours, every year from May to October. We apply the radar analysis hourly data on the central broader region with an area of 136 x 148 km2 , based on new data fusion rain code with multi-frame sequences. Finally we got some evidences and capabilities for strengthen forecasting range.