Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).

An Internet of Things based scalable framework for disaster data management

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economiclosses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefitedfrom the established development of smart city construction. And the IoT is visibly sensitive to the managementand monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to datastorage and data analysis. This article develops a new and much more general framework for disaster emergencymanagement under the IoT environment. The framework is a bottom-up integration of highly scalable Raw DataStorages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology foremergency disasters. Experimental results show that hybrid index and query technology have better performanceunder the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrievalfor the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-applicationsystem in this framework were performed based on the GPS trajectory data of 35,000 Beijing taxis and thevolumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability andhigher utility.