Multi-dimension Geospatial feature learning for urban region function recognition
This addresses the problem of monitoring and managing urban areas for urban planners and researchers, representing an incremental improvement through data fusion.
The paper tackled urban region function recognition by proposing a Multi-dimension Feature Learning Model (MDFL) that combines geospatial big data with remote sensing images, achieving an overall accuracy of 92.75% and outperforming state-of-the-art methods by 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.