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.
Highlights
Rapid development brings a more complex city
With point of interest (POI) and remote sensing imagers (RSI), Song et al carried out an experiment in Xiamen, China and proposed a method that introduce POI data to extract Urban functional zones (UFZ) based on remote sensing images(Song et al, 2018)
Several experiments are carried out, proving that the introduction of POI data and channel attention mechanism can effectively improve the UFZ classification accuracy based on RSI with increasing rate of 11.96% on kappa
Summary
Rapid development brings a more complex city. Urban functional zones (UFZ) like residential zone, commercial zone and industrial zone as the basic cells in the city, play an important role for city operation(Song et al, 2019). Of an object in image(Lowe, 2004, Yang, Newsam, 2010, Luo et al, 2013) Based on this view, many methods for UFZ classification were proposed(Zhang et al, 2018b, Zhang et al, 2018a). In NWPU-RESISC45 data set(Cheng et al, 2017) compared with other categories(Cheng et al, 2018) Finding that these methods are focus on objects relationship and visual feature in an imagery, it is easy to confuse scenes like commercial area and industrial area who are very similar visually and compositionally. When it comes to UFZ, no matter which part of a city, visual and object are similar. The double-blind peer-review was conducted on the basis of the full paper
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have