杨智文1,2 ,陈金云2 ,张志远3

　　(1.武汉大学遥感信息工程学院，湖北 武汉 430079;

　　2.重庆大学土木工程学院，重庆 400030;

　　3.水利部信息中心，北京 100053)

　　摘 要：针对传统建立水库相关数据库的方法多为线下人工统计，受到诸多因素制约的缺陷，将网络信息、电子地图及遥感影像等数据综合利用起来，实现水库自动提取。利用网络爬虫从官方水利政务网站抓取相关信息，筛选出最新的水库名称及所属地等数据，再根据水库名称及所属地，调用百度、高德等地图网站提供的应用接口，获取水库的空间坐标信息，并利用水库坐标对遥感影像做缓冲区分析，提取出水库水体范围，得到1个含有水库名称、坐标、面矢量图的水库基础数据库。经过验证，提取结果的查准率为0.9735，查全率为0.6113，作为二者的调和平均值达到0.751，能够完整监测并提取出大中型水库，但对小型水库的监测提取效果一般，可解决传统水库提取方法需要先验知识的问题，提高对水库的区域性动态监测能力。

　　关键词：水库;自动提取;网络信息;爬虫;遥感影像;水体提取;地图API

　　Research on automatic reservoir extraction method based on network information and remote sensing image

　　YANG Zhiwen1,2，CHEN Jinyun2，ZHANG Zhiyuan3

　　(1. School of Remote Sensing Information Engineering，Wuhan University，Wuhan 430079，China;

　　2. School of Civil Engineering，Chongqing University，Chongqing 400030，China;

　　3. Information Center， Ministry of Water Resources，Beijing 100053，China)

　　Abstract： In view of the shortcomings of traditional methods of developing reservoir-related database, which are mostly offline manual statistics and restricted by many factors, the study comprehensively uses data such as network information, electronic map and remote sensing image to realize automatic extraction of reservoirs’ information. The method uses web crawlers to grab relevant information from official water sector websites. The latest data such as the name and location of reservoirs are screened, and then, according to the name and location of the reservoir, the application interface provided by the map websites such as Baidu and Gaode is used to obtain the spatial coordinate information of the reservoir. The reservoir coordinates are used for buffer zone analysis on the remote sensing image to extract surface water area of the reservoir. Then a basic database of reservoir is obtained with regard to the name, coordinates and vector map of the reservoir. After verification, precision ratio of the extracted result is 0.9735, recall ration is 0.6113, and harmonic mean value of the two ratios is 0.751. The large and medium reservoirs can be completely monitored and extracted, however, the performance for small reservoirs is fair. This method solves the problem that priori knowledge is needed in traditional reservoir extraction methods and it improves the regional dynamic monitoring capability of reservoirs.

　　Key words：reservoir;automatic extraction;network information;crawler;remote sensing image;water body extraction;map API