Effective training strategies for deep-learning-based precipitation nowcasting and estimation

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that ...

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the in...

Nowcasting of Precipitation

Deep Learning for Laser Based Odometry Estimation

In this paper we take advantage of recent advances in deep learning techniques focused on image classification to estimate transforms between consecutive point clouds. A standard technique for feature learning is to use convolutional neural networks. Leveraging this technique can help with one of the biggest challenges in robotic motion planning, real time odometry. Sensors have advanced in rec...

Deep learning-based CAD systems for mammography: A review article

Breast cancer is one of the most common types of cancer in women. Screening mammography is a low‑dose X‑ray examination of breasts, which is conducted to detect breast cancer at early stages when the cancerous tumor is too small to be felt as a lump. Screening mammography is conducted for women with no symptoms of breast cancer, for early detection of cancer when the cancer is most treatable an...