The Role of Remote Sensing Feature Extraction in Smart Cities: A Case Study of the Xiangyang Area

Abstract

This paper takes Xiangyang City, Hubei Province, as an example to explore the role of remote sensing feature extraction technology in smart city construction. The study is based on GF-4 (Gaofen-4) remote sensing imagery. Supervised classification was performed using ENVI software. Features such as vegetation, water bodies, and bare land were extracted through band fusion, standard false-color composition, and the Maximum Likelihood method. Classification accuracy was verified using a confusion matrix. Thematic maps were created using ArcGIS, and land use proportions were calculated (e.g., water bodies accounting for 65.3%). The results indicate that remote sensing technology can efficiently quantify current urban land use, providing precise spatial data support for smart city planning and facilitating optimized resource allocation and sustainable development. Moreover, the approach highlights the potential of integrating multi-source remote sensing with geographic information systems for dynamic urban monitoring. It also emphasizes the scalability of the method for application in other medium-sized Chinese cities. Finally, the findings provide practical guidance for decision-makers seeking to balance ecological protection and urban growth under the framework of smart city development.