论文标题
多维地理空间特征学习城市地区功能识别
Multi-dimension Geospatial feature learning for urban region function recognition
论文作者
论文摘要
城市地区功能识别在监测和管理有限的城市地区时起着至关重要的特征。由于城市功能很复杂，并且充满了社会经济属性，因此只需使用配备物理和光学信息的遥感图像（RS）图像就无法完全解决分类任务。另一方面，随着移动通信和互联网的发展，地理空间大数据（GBD）的获取变得可能是可能的。在本文中，我们建议使用高维GBD数据与RS图像结合使用以用于城市区域功能识别的RS图像，提出了多维特征学习模型〜（MDFL）。当提取多维功能时，我们的模型考虑了由其活动建模的用户相关信息以及从区域图中抽象的基于区域的信息。此外，我们提出了一个决策融合网络，该网络集成了来自多个神经网络和机器学习分类器的决策，并且最终决定是考虑RS图像中的视觉提示以及GBD数据中的社交信息。通过定量评估，我们证明了我们的模型在92.75的总体准确性上的表现优于最先进的时间10％。
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.
