# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
## 1 Introduction  
In the field of Earth sciences, the rapid development of modern remote-sensing technology has led to a continuous improvement in the ability of satellites and sensors to acquire data, and the amount of acquired remote-sensing image data has steadily increased [1, 2]. However, in the face of different surface information diversity and global needs, multi-dimensional remote-sensing applications [3, 4, 5] are complex. In particular, many applications, such as earthquakes [6], flood monitoring [7], and monitoring of other disasters [8, 9, 10], require real-time or near real-time processing capabilities. Therefore, effective processing of disaster-monitoring applications put forward requirements for increased computing power. For decades, high-performance data-intensive computing methods for cluster computing, distributed computing, and data centers [11, 12, 13, 14] have been widely used in disaster-monitoring applications. However, as the size of computers increases, the maintenance of cluster systems has become difficult and costly [15]. Subsequently, a new model of computing--cloud computing--has been developed. Cloud computing is a service related to information technology, software, and the Internet. Cloud computing brings together many computing resources and implements automated management through software. The advantages of cloud computing are high flexibility, scalability, and high performance. The present time can be considered to be the Internet era; as part of this, cloud computing, based on the Internet as its computing platform, enhances the application of large-scale disaster monitoring. As a result of this development, an increasing amount of research has been conducted on cloud computing-based remote-sensing image processing and application systems. These studies [16; 17; 18] show that the cloud computing architecture provides an excellent solution for improving the speed of remote-sensing image processing and network sharing of remote-sensing data using its inherent data parallel technology. However, determining a means of fully using the distributed parallel organization of the cloud computing architecture remains a challenge, and it is difficult to implement cloud computing for remote-sensing processing. This limits the development and widespread application of cloud computing in disaster-monitoring applications. In addition, algorithms for disaster monitoring are highly coupled to distributed parallel strategies for cloud computing. In this case, the parallel strategy and code of the cloud computing algorithm for disaster-monitoring applications is usually not reusable. As a result, a key question in disaster-monitoring applications relates to the choice of programming model so that the algorithm is described clearly, more convenient to develop, and more efficient to operate. Regarding the above issues, this paper attempts to explore general programming models of cloud computing for disaster-monitoring applications. The proposed models will provide a simple and effective way to implement and write code for disaster-monitoring applications in cloud architectures. With the aim of processing disaster-monitoring applications based on a variety of scientific models, this study developed a general cloud computing model. First, this study analyzed the characteristics of current disaster-monitoring algorithms, and then classified these algorithms based on their computational features. Various disaster-monitoring models were then analyzed, and multiple sets of computing models for various scientific models were designed. In this paper, the cloud computing description of the disaster-monitoring application model is provided. Based on the mature programming model MapReduce, a cloud computing model suitable for distributed processing of disaster-monitoring models was studied. This computing mode provides a high level of abstraction.