College of Water Conservancy, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam.
Comput Intell Neurosci. 2022 Aug 29;2022:7886358. doi: 10.1155/2022/7886358. eCollection 2022.
Through a comprehensive theoretical basis and actual test analysis of the application system design and functional efficiency of the cloud platform, this paper puts forward an artificial intelligence environmental data monitoring and wetland environmental simulation method based on GIS remote sensing images. First, the basic storage and computing functions have been enhanced at the physical layer. Second, the middleware layer is more flexible in the use of management methods and strategies. There are many strategies and methods that can be used in combination. Finally, based on this, the application system design framework is more convenient and faster so that you can focus on business logic, and the strategic advantages of certain functions are very obvious. The method of object-oriented classification and visual interpretation using UAV image data and satellite remote sensing images from the typical recovery area and treatment area of wetland from 2016 to 2020 is given in detail together to extract wetland information and use GIS software for dynamic calculation. Using the wetland transmission matrix method, the distribution map of the characteristic types of the survey areas in the four periods and the conversion status of the characteristic types at each stage were obtained, and the effect of wetland treatment was quantitatively studied.
通过对云平台的应用系统设计和功能效率进行全面的理论基础和实际测试分析,本文提出了一种基于 GIS 遥感图像的人工智能环境数据监测和湿地环境模拟方法。首先,在物理层增强了基本存储和计算功能。其次,在中间件层,管理方法和策略的使用更加灵活,可以结合使用多种策略和方法。最后,在此基础上,应用系统设计框架更加方便快捷,可以专注于业务逻辑,并且某些功能的战略优势非常明显。详细介绍了使用无人机图像数据和卫星遥感图像从 2016 年至 2020 年湿地典型恢复区和处理区的面向对象分类和目视解译方法,以提取湿地信息并使用 GIS 软件进行动态计算。利用湿地传递矩阵法,得到了四个时期调查区特征类型的分布图和各阶段特征类型的转换状况,定量研究了湿地处理的效果。