Urban region function recognition plays a vital character in monitoring and managing the limited urban areas. Since urban functions are complex and full of social-economic properties, simply using remote sensing~(RS) images equipped with physical and optical information cannot completely solve the classification task. On the other hand, with the development of mobile communication and the internet, the acquisition of geospatial big data~(GBD) becomes possible. In this paper, we propose a Multi-dimension Feature Learning Model~(MDFL) using high-dimensional GBD data in conjunction with RS images for urban region function recognition. When extracting multi-dimension features, our model considers the user-related information modeled by their activity, as well as the region-based information abstracted from the region graph. Furthermore, we propose a decision fusion network that integrates the decisions from several neural networks and machine learning classifiers, and the final decision is made considering both the visual cue from the RS images and the social information from the GBD data. Through quantitative evaluation, we demonstrate that our model achieves overall accuracy at 92.75, outperforming the state-of-the-art by 10 percent.

翻译：城市区域功能的确认在监测和管理有限的城市地区方面起着关键作用。由于城市功能复杂,充满了社会经济特性,仅仅利用遥感~(RS)图像,并配备了物理和光学信息,并不能完全解决分类任务。另一方面,随着移动通信和互联网的发展,获取地理空间大数据~(GBD)成为可能。在本文件中,我们提议采用多维的GBD数据,结合RS图像,为城市地区提供高维的GBD数据,使用多维的GBD学习模型~(MDFL)。在提取多维功能时,我们的模型考虑到以其活动为模型的用户相关信息,以及从区域图中提取的基于区域的信息。此外,我们提议建立一个决定融合网络,将若干神经网络和机器学习分类器的决定结合起来,最后决定是考虑到RS图像的视觉提示和GBD数据的社会信息。通过定量评估,我们证明我们的模型在92.75上实现了总体精确度,比区域图高出10%的状态。