With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge with great social and economic impact. The radar echo extrapolation approaches for precipitation nowcasting take radar echo images as input, aiming to generate future radar echo images by learning from the historical images. To effectively handle complex and high non-stationary evolution of radar echoes, we propose to decompose the movement into optical flow field motion and morphologic deformation. Following this idea, we introduce Flow-Deformation Network (FDNet), a neural network that models flow and deformation in two parallel cross pathways. The flow encoder captures the optical flow field motion between consecutive images and the deformation encoder distinguishes the change of shape from the translational motion of radar echoes. We evaluate the proposed network architecture on two real-world radar echo datasets. Our model achieves state-of-the-art prediction results compared with recent approaches. To the best of our knowledge, this is the first network architecture with flow and deformation separation to model the evolution of radar echoes for precipitation nowcasting. We believe that the general idea of this work could not only inspire much more effective approaches but also be applied to other similar spatio-temporal prediction tasks.

Urban sensing is one of the fundamental building blocks of urban computing. It uses various types of sensors deployed in different geospatial locations to continuously and cooperatively monitor the natural and cultural environment in urban areas. Nevertheless, issues such as uneven distribution, low sampling rate and high failure ratio of sensors often make their readings less reliable. This paper provides an innovative framework to detect the noise data as well as to repair them from a spatial-temporal causality perspective rather than to deal with them individually. This can be achieved by connecting data through monitored objects, using the Skip-gram model to estimate spatial correlation and long short-term memory to estimate temporal correlation. The framework consists of three major modules: 1) a space embedded Bidirectional Long Short-Term Memory (BiLSTM)-based sequence labeling module to detect the noise data and the latent missing data; 2) a space embedded BiLSTM-based sequence predicting module calculating the value of the missing data; 3) an object characteristics fusion repairing module to correct the spatial and temporal dislocation sensory data. The approach is evaluated with real-world data collected by over 3000 electronic traffic bayonet devices in a citywide scale of a medium-sized city in China, and the result is superior to those of several referenced approaches. With a 12.9% improvement in data accuracy over the raw data, the proposed framework plays a significant role in various real-world use cases in urban governance, such as criminal investigation, traffic violation monitoring, and equipment maintenance.