【摘要】 中国城镇化和智慧城市建设的推进,对城市精细化规划与管理提出新挑战。明确城市空间结构划分,加强城市功能区的合理规划,对城镇化建设具有重要意义。基于遥感图像数据、POI(point of interest)数据及路网数据,使用遥感信息提取技术和语义信息挖掘方法,实现城市功能区的语义分类。对随机挑选的360处区块进行样本验证,结果显示城市功能语义分区的精度达到87.5%。该方法受区域限制较少,对城市功能分区研究有效。更多还原

【Abstract】 As the urbanization and the policy of smart city advance step by step, new challenges are put forward for the meticulous planning of cities. It is of great significance to clarify the division of urban spatial structure and strengthen the rational planning of urban functional areas. We obtain the semantic classification results of urban functional areas using the remote sensing technology and the semantic information mining method based on the GF-1 image, POI(point of interest) data, and road network data. Firstly, extraction of construction land in study area is based on object-oriented method, and the block partition is recognized by using road network data. Considering that the semantic features from POI data make fine classification of urban construction land, we estimate POI data for each category using kernel density analysis. Then the evaluation model of function area category is established based on the overlapping regions of multiple types of kernel density. Thus the function land classification of study area is completed. 360 blocks of plots are randomly selected for sample verification test. The results show that the definition of urban function areas is accurate and the accuracy of urban function zoning is as high as 87.5%.更多还原