Social Information Fused Urban Functional Zones Classification Network

Abstract

Abstract. Fast-changing cities need efficient management. Accurate classification of urban functional zone (UFZ) can provide important reference for cities management. Remote sensing imagery (RSI) is large scale, high resolution and fast update, which can provide massive data for UFZ extraction. However, UFZ are more concerned with social attributes such as industrial production and commercial activities, while images can only provide visual features, which is not enough for an elaborate UFZ classification. To solve this problem, in this paper, we combine RSI and point of interest (POI) data together for UFZ classification, and propose a Social Information Fused Urban Functional Zones Classification Network (SIF-Net). For RSI, we simply use a Xception CNNs network extract the visual information. For POI data, we first build a coarse heatmap for each type of POI (e.g. retail, apartment…), and then combine them as a POI tensor. Afterward, we use a channel attention module (CAM) based CNN model to fuse heatmaps from each type of POI, and then build a fine distribution of UFZ as the social information. Finally, we fuse the visual information extracted from RSI and social information extracted from POI by concatenating them. By fusing this two complementary information, our method makes up for the shortcomings of extracting UFZ based on RSI and general CNNs only. Compared with current state-of-the-art methods, experiments show that the proposed SIF-Net can significantly improve the UFZ classification result.

Urban Functional Zone Classification Using Light-Detection-and-Ranging Point Clouds, Aerial Images, and Point-of-Interest Data

“…Research suggests that the integration of high-resolution remote sensing images and POI data to construct UFZ classification grids can significantly enhance classification accuracy [15,16]. Furthermore, a hierarchical semantic cognition approach has been proposed as a comprehensive cognitive framework for UFZ identification [17]. Nonetheless, these studies did not comprehensively analyze the spatial morphological differences between UFZs, especially in terms of three-dimensional (3D) morphological features.…”