| 城市功能区语义信息挖掘与遥感分类 |
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| 作者姓名： | 李娅 刘亚岚 任玉环 王智灏 曲畅 |
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| 作者单位： | 1. 中国科学院遥感与数字地球研究所, 北京 100101;
2. 中国科学院大学, 北京 100049;
3. 北京大学地球与空间科学学院遥感与地理信息系统研究所, 北京 100871 |
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| 基金项目： | 国家自然科学基金青年基金（41601387）资助 |
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| 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。
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| 关 键 词： | 城市功能分区 建设用地提取 POI数据 核密度估计 语义信息挖掘 |
| 收稿时间： | 2017-10-24 |
| 修稿时间： | 2018-01-19 |
Semantic information mining and remote sensing classification of urban functional areas |
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| Authors: | LI Ya LIU Yalan REN Yuhuan WANG Zhihao QU Chang |
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| Institution: | 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Institute of Remote Sensing and Geographical Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China |
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| 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%. |
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| Keywords: | urban functional areas construction land extraction POI data kernel density estimation semantic information mining |
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