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High resolution remote sensing image cloud removal can bring a lot of convenience for human activities. However, the existing cloud removal algorithms have a variety of disadvantages. First of all, they have the disadvantages of long computing time and large consumption of computing resources. Secondly, the effect of recovery needs to be improved. In order to improve the above two points, a near real‐time effective algorithm is proposed, namely MSAR‐Defognet (multiple scale attention residual network using for cloud remove), which consumes less computing power and space and has superior cloud removal effect. On the one hand, several different large‐scale filters are chosen to extract the weak information effectively, while can save the computing power and shorten the image processing time. On the other hand, the fine‐grained convolution residual block with channel attention mechanism is used to enhance the network's ability to extract cloud features. In addition, a data set which is closer to the real cloud shape and has higher richness to train the cloud removal network, so that the parameters obtained by training have stronger robustness and can adaptively remove clouds with different thickness. Experiments show that, compared with other advanced network models, the network not only has the advantage of fast processing speed, but also has better image restoration effect in high‐resolution remote sensing image restoration. It can meet the requirements of many hard real‐time tasks, so that remote sensing images can play a greater value for human activities. [ABSTRACT FROM AUTHOR]

Summary: With the development of smart Internet of things devices, intelligent applications are expected to lead further innovation in smart city. However, although cloud computing infrastructure can be used to meet traditional challenges, the scheduling model for new big data intelligent application has still not matured. In this work, we proposed a two‐stage scheduling framework for smart city intelligent application. In the first stage, we propose a virtual machine selection algorithm for edge computing to enhance relative migration benefits. The algorithm defines the invalid virtual machine migration and relative migration benefits from the change in the overall computing resources of the cloud data center after the virtual machine migration. In the second stage, we proposed an energy efficient and resource‐constrained scheduling framework for edge computing. The historical data of the cloud and edge computing workload of the computing node are processed in a sliding window manner, and the median absolute deviation of the historical data is used as the base of the physical reserved resource constraint when the base also changes as the workload changes. The experimental results show that energy‐efficient and resource‐constrained can make the computer resource provide high‐quality services for users in a low‐energy state. [ABSTRACT FROM AUTHOR]

Abstract DBSCAN is a density-based data clustering algorithm, in image processing, data mining, machine learning and other fields are widely used. With the increasing of the size of clusters, the parallel DBSCAN algorithm is widely used. in image processing, data mining, machine learning and other fields are widely used. However, we consider current partitioning method of DBSCAN is too simple and steps of GETNEIGHBORS query repeatedly access the data set on spark. So we proposed DBSCAN-PSM which applies new data partitioning and merging method. In the first stage of our method we import the KD-Tree, combine the partitioning and GETNEIGHBORS query, reduce the number of access to the data set and decrease the influence of I/O in the algorithm. In the second stage of our method we use the feature of points in merging so as to avoid the time costing of the global label. Experimental results showed that our new method can improve the parallel efficiency and the clustering algorithm performance. [ABSTRACT FROM AUTHOR]

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability. [ABSTRACT FROM AUTHOR]

With the development of network communication, a 1000-fold increase in traffic demand from 4G to 5G, it is critical to provide efficient and fast spatial data access interface for applications in mobile environment. In view of the low I/O efficiency and high latency of existing methods, this paper presents a memory-based spatial data query method that uses the distributed memory file system Alluxio to store data and build a two-level index based on the Alluxio key-value structure; moreover, it aims to solve the problem of low efficiency of traditional method; according to the characteristics of Spark computing framework, a data input format for spatial data query is proposed, which can selectively read the file data and reduce the data I/O. The comparative experiments show that the memory-based file system Alluxio has better I/O performance than the disk file system; compared with the traditional distributed query method, the method we proposed reduces the retrieval time greatly. [ABSTRACT FROM AUTHOR]